WO2023091455A1

WO2023091455A1 - Quality control of user-generated biological sample cards

Info

Publication number: WO2023091455A1
Application number: PCT/US2022/050057
Authority: WO
Inventors: Michal DEPA; Soumil CHUGH; Sidhant JENA; Javier J. Echenique; Shunsuke Matsuoka; Theresa TRIBBLE
Original assignee: General Prognostics Inc
Current assignee: General Prognostics Inc
Priority date: 2021-11-17
Filing date: 2022-11-16
Publication date: 2023-05-25
Anticipated expiration: 2024-05-17

Abstract

Methods, systems, and computer readable storage medium storing processor executable instructions for evaluating the quality of a sample card comprising a biological sample. A method of evaluating the quality of a sample card comprising a biological sample and/or an image of the sample card: determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique; determining at least one characteristic of the serum zone based on the identified location of the serum zone; and determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone.

Description

QUALITY CONTROL OF USER-GENERATED BIOLOGICAL SAMPLE CARDS

RELATED APPLICATION

This application claims priority to Indian Provisional Patent Application Serial No. 202111052785 filed on November 17, 2021, in the Indian Intellectual Property Office, which is incorporated by reference herein in its entirety.

BACKGROUND

Sample cards including dried blood spot (DBS) cards have allowed for at-home diagnostic testing to become readily accessible. Patients in need of at-home diagnostic testing can prepare sample cards containing a biological sample (e.g., blood) and mail that sample card to a laboratory testing site. The existence of such sample cards and processes have made at- home diagnostics a significant segment of all diagnostic testing.

SUMMARY

Methods, systems, and computer readable storage medium storing processor executable instructions for evaluating the quality of a sample card comprising a biological sample (e.g., a blood and/or plasma sample on a dried blood/plasma spot card) are provided herein.

Accordingly, in some aspects, the disclosure provides a method of evaluating the quality of a sample card comprising a biological sample and/or an image of the sample card. In some embodiments, a method of evaluating the quality of a sample card comprising a biological sample and/or an image of the sample card comprises using at least one computing device to perform: capturing an image of the sample card using a camera of the portable computing device or a scanner communicatively coupled to the portable computing device; determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique; determining at least one characteristic of the serum zone based on the identified location of the serum zone; and determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone.

In some embodiments, the at least one computing device further performs: providing instructions to a user based on the measure of quality of sample card and/or the image of the sample card. In some embodiments, the method further comprises preparing the sample card prior to obtaining the image by applying a liquid biological sample to the sample application zone and allowing the sample to flow across or within the sample card. In some aspects, the disclosure provides a method comprising applying a liquid biological sample from a subject to a sample application zone of a sample card; and using at least one computing device to perform: capturing an image of the sample card using a camera of the computing device or a scanner communicatively coupled to the computing device; determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique; determining at least one characteristic of the serum zone based on the identified location of the serum zone; determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone; and providing instructions to a user based on the measure of quality of the sample card and/or the image of the sample card.

In some embodiments, the instructions to the user comprise: (i) instructions to obtain a new biological sample on an unused sample card, (ii) instructions to deliver the sample card to a laboratory for analysis of the biological sample, or (iii) instructions to analyze the biological sample. In some embodiments, a new biological sample is applied to a sample application zone of an unused sample card when the instructions to the user comprise: (i) instructions to obtain a new biological sample on an unused sample card, wherein the new biological sample is a second liquid biological sample from the same subject. In some embodiments, the sample card is delivered to a laboratory for analysis of the biological sample when the instructions to the user comprise: (ii) instructions to deliver the sample card to a laboratory for analysis of the biological sample. In some embodiments, the biological sample is analyzed when the instructions to the user comprise: (iii) instructions to analyze the biological sample.

In some embodiments, capturing an image of the sample card comprises capturing a photograph of the sample card or scanning the sample card.

In some embodiments, determining the location of the serum zone comprises: determining a location of the sample application zone using an edge detection technique; and determining the location of the serum zone based on the location of the sample application zone and information indicating a relative location of the serum zone to the sample application zone.

In some embodiments, the method further comprises: determining at least one characteristic of the sample application zone based on the identified location of the sample application zone.

In some embodiments, determining the location of the serum zone comprises: determining a location of the sample flow zone using an edge detection technique; and determining the location of the serum zone based on the location of the sample flow zone and information indicating a relative location of the serum zone to the sample flow zone. In some embodiments, the method further comprises: determining at least one characteristic of the sample flow zone based on the identified location of the sample flow zone.

In some embodiments, the biological sample is whole blood, plasma, serum, urine, saliva, or any other body fluid. In some embodiments, the biological sample is a sample from a subject, wherein the subject is a human, rodent, primate, dog, cat, bird, horse, cow, sheep, pig, veterinary animal, or any other mammal.

In some embodiments, the sample card is an ADx 100 sample card. In some embodiments, the sample card is a rectangle or a square. In some embodiments, the sample card has a length of 6-20 cm and a width of 2-10 cm.

In some embodiments, the computing device is a portable computing device (e.g., a mobile computing device or tablet, smartphone, or laptop).

In some embodiments, the edge detection technique comprises Canny edge detection, a Hough transform, Sobel edge detection, Prewitt edge detection, and/or Laplacian edge detection. In some embodiments, the edge detection technique further comprises erosion or dilation of the identified edges.

In some embodiments, the dimensions of the sample card are determined by identifying the edges of the largest rectangle or square. In some embodiments, the dimensions of the sample card correspond to the dimensions used by a known sample card manufacturer. In some embodiments, the dimensions of the sample card allow for identification of the brand, manufacturer and/or model of the sample card.

In some embodiments, the location of the sample application zone is determined by identifying the edges of the largest rectangle or square contained within a predetermined region- of-interest of the sample card. In some embodiments, the location of the sample application zone is in a fixed location within the dimensions of the sample card.

In some embodiments, following obtaining the image of the sample card, the image of the sample card is warped using an image analysis technique such that the dimensions of the sample card are flattened into a perfect rectangle or square.

In some embodiments, a sample flow zone is positioned between a sample application zone and the serum zone. In some embodiments, following determining a location of the sample application zone and/or the sample flow zone in the sample card, a serum region-of-interest comprising the serum zone is extracted from the image.

In some embodiments, the serum region-of-interest does not include any portion of the sample application zone or the sample flow zone. In some embodiments, a location of the serum zone is determined by applying a segmentation technique to a serum region-of-interest. In some embodiments, the segmentation technique is a watershed segmentation technique, a deformable model, or a texture analysis. In some embodiments, the deformable model is an active shape or contour model, or a deformable model based on level sets.

In some embodiments, the segmentation technique utilizes seed points for the serum zone and background. In some embodiments, the seed points for the serum zone are positioned from the edge of the sample flow zone or the serum region-of-interest by a distance that is equal to the height of the sample flow zone. In some embodiments, the seed points for the serum zone are positioned in a fixed location relative to the location of the sample flow zone or the serum region- of-interest. In some embodiments, the seed points for the background are positioned in a fixed location relative to the location of the sample flow zone or the serum region-of-interest. In some embodiments, the seed points for the serum zone constitute a line that is positioned at a fixed distance from the edge of the sample flow zone or serum region-of-interest. In some embodiments, the fixed location of the seed points for the serum zone is 0-25 mm from an edge of the sample flow zone or an edge of the serum region-of-interest and/or is equidistant from two edges of the serum region-of-interest and/or is parallel to the an edge of the serum region-of- interest. In some embodiments, the fixed location of the seed points for the background is 0-10 mm from an edge of the serum region-of-interest and/or is equidistant from two edges of the serum region-of-interest and/or is perpendicular to an edge of the serum region-of-interest.

In some embodiments, the at least one characteristic of the zone(s) comprise color of the sample within the sample application zone, color of the sample within the sample flow zone, color of the serum zone, percentage of area within the sample application zone that contains sample, area of the sample flow zone, area of the serum zone, and/or number of discrete shapes within the serum zone.

In some embodiments, the color of the serum zone indicates the amount of residual red blood cells present within the serum zone. In some embodiments, the discrete shapes within the serum zone are squares, rectangles, or circles, optionally wherein the squares are 10x10 mm squares.

Some aspects of the disclosure provide a system, comprising: a computing device; and at least one non-transitory computer readable storage medium storing processor executable instructions that, when executed by the computing device, cause the computing device to perform: capturing an image of the sample card using a camera of the computing device or a scanner communicatively coupled to the portable device; determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique; determining at least one characteristic of the serum zone based on the identified location of the serum zone; determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone.

In some embodiments, the at least one computing device further performs: providing instructions to a user based on the measure of quality of the sample card and/or the image of the sample card.

Yet other aspects of the disclosure provide at least one non-transitory computer readable storage medium storing processor executable instructions that, when executed by a computing device, cause the computing device to perform: capturing an image of the sample card using a camera of the computing device or a scanner communicatively coupled to the portable device; determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique; determining at least one characteristic of the serum zone based on the identified location of the serum zone; determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of an illustrative process 100 for evaluating the quality of a sample card comprising a biological sample and/or an image of the sample card, according to some embodiments of the technology described herein.

FIG. 2 is a flowchart illustrating an example implementation of act 104 of process 100 shown in FIG. 1, according to some embodiments of the technology described herein.

FIG. 3A is a flowchart illustrating an example implementation of act 106 of process 100 shown in FIG. 1, according to some embodiments of the technology described herein.

FIG. 3B is a flowchart illustrating an example implementation of act 302 shown in FIG. 3A, according to some embodiments of the technology described herein.

FIG. 3C is a flowchart illustrating an example implementation of act 308 shown in FIG. 3A, according to some embodiments of the technology described herein.

FIG. 4 provides an example of a sample card comprising a blood sample. The location of the sample application zone, sample flow zone and serum zone are indicated.

FIG. 5 provides an exemplary method of the disclosure. Provided at top are acts of the exemplary method from collection of an input image of a sample card comprising a biological sample (e.g., blood) to detection of the sample application zone using an edge detection technique (e.g., Canny edge detection). Provided at bottom are acts of the exemplary method from detection of the sample flow zone (e.g., based on known relative position from sample application zone) to the detection of the serum zone using a segmentation technique (e.g., watershed segmentation technique) to determination of a measure of quality of the sample card and/or the image of the sample card based the serum zone.

FIG. 6 provide schematics of an exemplary system that can be used in implementing selected embodiments of the disclosure.

DETAILED DESCRIPTION

Described herein are methods, systems, and computer readable storage medium storing processor executable instructions for evaluating the quality of a sample card comprising a biological sample (e.g., a blood and/or plasma sample on a dried blood/plasma spot card).

The inventors realized that errors by patients (e.g., patients in need of at-home diagnostic testing) who prepare user-generated sample cards containing a biological sample (e.g., blood) prior to submission of said sample cards to a laboratory testing site can lead to high rates of said sample cards being discarded by the laboratory testing site. When a sample card is discarded, several days may lapse before the patient becomes aware of this fact. The patient may then be asked to repeat the process, or if time has become more critical, may be asked to expedite the testing process by physically visiting a diagnostic facility. This delay is an inconvenience and financial drain on the patients and the laboratory testing sites. It can also result in a missed testing time point, and in turn and can result in increased health risks for the patient. In response to this technical problem, the inventors developed methods for reducing the rates of discarded sample cards by the laboratory testing site and increasing the average quality of the usergenerated sample cards. These methods can also be used to help laboratory sites perform incoming inspection of sample cards in a simpler and objective manner.

The inventors have developed techniques for determining whether the quality of a sample card comprising a biological sample (e.g., a biological sample from a user) is sufficient to enable downstream analysis of the biological sample. These methods utilize a unique and specific combination of software techniques (e.g., image-processing techniques) that determine the locations (e.g., position, shape, and size) of discrete zones within the user-generated sample card comprising a biological sample, including a serum zone, which is necessary for determining a measure of quality of the sample card and/or the image of the sample card.

Conventional techniques for evaluating sample card quality involve edge detection techniques. Such conventional techniques provide poor results, often failing to accurately determine the quality of a sample card, because the serum region-of-interest (which comprises the serum zone) on sample cards (e.g., ADx 100 sample cards) has a textured appearance that causes edge detection techniques to produce noisy outputs with spurious edges. This noise leads to incorrect or inaccurate determination of the location of the serum zone, and consequently to incorrect or inaccurate determinations of the quality of the sample card and/or the image of the sample card.

The inventors have recognized that determination of the location of a serum zone is useful for highly accurate determinations of the quality of a sample card comprising a biological sample. As a result of that recognition, the inventors developed techniques for determining sample card quality that involve, for example, the use of a segmentation technique (e.g., a watershed segmentation technique) to determine the location of the serum zone. Furthermore, the inventors realized that the location of the seed points for use in the segmentation technique was important for providing accurate results. In some embodiments, the inventors utilize seed points that are dynamically located relative to the locations of the sample flow zone, serum region-of-interest and/or dimensions of the sample flow zone as described in this disclosure. Finally, the inventors realized that it was important that the segmentation technique is only applied within a serum region-of-interest that is first determined (and not an entire image, or an image of the entire sample card).

One of the key benefits of the methods described herein is to empower and equip individual users (e.g., human patients) with the capability to validate that they have adequately prepared a sample card (e.g., prepared a sample card with a sample of their blood and/or plasma) before submitting said sample card to an external laboratory (e.g., sending the sample card to an external laboratory through physical mail) for downstream analysis. If the user-generated sample card is inadequate for downstream analysis, then the computing device that is performing the acts of the method may instruct the user to prepare a new sample card comprising a new biological sample.

In some embodiments, process 100 begins at act 102 for applying a biological sample from a subject (e.g., a human subject) to a sample application zone of a sample card. In some embodiments, act 102 comprises applying a liquid biological sample. As described elsewhere, a biological sample that is applied to a sample application zone is whole blood, plasma, serum, urine, saliva, or any other body fluid. For example, in some embodiments, a biological sample is whole blood from a human subject. In some embodiments, act 102 further comprises an incubation or waiting period following the initial application of the biological sample to the sample application zone. The conditions of the incubation or waiting period, such as the length of time, may be provided by the manufacturer or distributor of the sample card, and may be provided as instruction to the user by the device performing process 103. In some embodiments, the length of the incubation or waiting period is at least 5, 10, 20, 30, or 60 minutes. The incubation or waiting period may allow for the biological sample (or components of the biological sample) to travel away from the sample application zone along a length of the sample card or within the sample card. The incubation or waiting period may also allow for a liquid biological sample to dry.

Acts 104, 106, 108, 110, and 112 are performed by one or more devices (block 103). In some embodiments, the one or more devices are one or more computing devices (e.g., portable computing devices), camera, and/or scanner. In some embodiments, block 103 comprises a portable computing device comprising a camera and a second computing device, wherein the portable computing device and the second computing device are connected via cloud computing and share data. In some embodiments, block 103 comprises a camera and a computing device, wherein the image is captured using the camera and subsequently delivered to the computing device by conventional file sharing techniques. In some embodiments, block 103 comprises computing devices, cameras, and image capture. Further discussion of the devices for use in block 103 are described in the section of this disclosure entitled “Computing devices, cameras, and image capture” and elsewhere in this disclosure.

After applying the biological sample from a subject to a sample application zone, process 100 proceeds to act 104 for capturing an image of the sample card comprising a biological sample. In other embodiments, process 100 begins at act 104.

An image may be captured using any camera or scanner. In some embodiments, an image of the sample card is captured using a camera or scanner that obtains a static image. In some embodiments, an image of the sample card is captured using a camera or scanner that obtains a dynamic image (e.g., using a document scanning application). In some embodiments, an image may be captured from a server storing the image. Techniques for capturing the image of the sample card are described herein including with respect to FIG. 2.

After capturing the image, process 100 proceeds to act 106 for determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique. A serum zone is as described in the section of this disclosure entitled “Serum Zone” and elsewhere in this disclosure. The location of the serum zone may be determined using an edge detection technique and a segmentation technique as described herein. Techniques for determining a location of a serum zone in the sample card are described herein including with respect to FIG. 3A. After determining a location of a serum zone, process 100 proceeds to act 108 for determining at least one characteristic of the serum zone based on the identified location of the serum zone. For example, act 108 may comprise determining the color of the serum zone, total area of the serum zone, and/or number of discrete shapes within the serum zone. In some embodiments, determining the color of the serum zone involves determining the level of opacity of the serum zone. In some embodiments, determining the color of the serum zone involves determining the average color across the entire area of the serum zone. In some embodiments, determining the color of the serum zone involves determining the percentage of the total area of the serum zone that contains color (e.g., color different from background of sample card). The serum zone may be uniformly colored across its entire area or contain different shades or levels of opacity across its area. In some embodiments, determining the total area of the serum zone involves determining the shape and size of the serum zone. In some embodiments, determining the number of discrete shapes within the serum zone involves overlaying the serum zone with discrete shapes (e.g., squares, rectangles or circles) having specific dimensions (e.g., 10x10 mm squares). In some embodiments, determining the number of discrete shapes within the serum zone involves dividing the total area of the serum zone by the area of a discrete shape. Other aspects of the characteristics of the serum zone are described in the section of this disclosure entitled “Serum Zone” and elsewhere in this disclosure.

After determining at least one characteristic of the serum zone, process 100 proceeds to act 110 for determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone. For example, act 110 can determine a measure of quality based on the color of the serum zone, total area of the serum zone, and/or number of discrete shapes within the serum zone. In some embodiments, act 110 may further comprise determining a measure of quality based in part on the characteristics of the sample application zone and/or the sample flow zone.

In some embodiments, a measure of quality that exceeds a threshold level (e.g., based on a single characteristic or a combination of characteristics) indicates that the sample card can be used for downstream analysis (e.g., at a laboratory). In some embodiments, a measure of quality that does not meet a threshold level indicates that the sample card cannot be used for downstream analysis and should be replaced with a new sample card comprising a new biological sample from the subject. In some embodiments, a measure of quality that does not meet a threshold level indicates that a new image of the sample card should be captured and analyzed as described herein. In some embodiments, a specific threshold level will depend on the diagnostic assay or test that is going to be performed with the sample card. For example, if the diagnostic assay or test requires a large amount of sample, then the threshold level(s) may need to be increased. In some embodiments, a threshold level might be individually determined (e.g., in real-time) for a particular subject based on the type or number of diagnostic assays or tests that are required.

In some embodiments, a threshold level related to the color of the serum zone is based on the average color (e.g., opacity or color different from color of background) across the entire area of the serum zone. In some embodiments, a threshold level related to the color of the serum zone is based on the percent of the total area of the serum zone that contains a certain amount of color (e.g., color different from color of background). In some embodiments, a threshold level related to the color of the serum zone is based on the ratio of the average color across the entire area of the serum zone in a certain color channel (e.g., red channel) and the average color across another area or areas, such as the sample application zone and/or sample flow zone, in the same color channel. In some embodiments, this ratio should be less than 5%. For example, for samples of whole blood, the average red channel intensity across the entire area of the serum zone should be less than 5% of the average red channel intensity across the entire area of the sample application zone and/or sample flow zone. In some embodiments, a threshold level related to the color of the serum zone is based on the amount of unwanted cells in the serum zone (e.g., unwanted red blood cells). In some embodiments, a threshold level related to the total area of the serum zone is based on the amount of serum needed for downstream analyses. In some embodiments, a threshold level related to the number of discrete shapes within the serum zone is based on the minimum requirements needed for downstream analyses. In some embodiments, at least one or at least two discrete shapes of a certain size are needed for downstream analyses. In some embodiments, at least one or at least two 10x10 mm squares are needed for downstream analyses.

After determining a measure of quality of the sample card and/or the image of the sample card, process 100 proceeds to act 112 for providing instructions to a user based on the measure of quality. In some embodiments, act 112 comprises providing instructions to the user to prepare a new sample card comprising a new biological sample for the subject if the measure of quality does not meet a threshold level. In some embodiments, act 112 comprises providing instructions to the user to capture a new image of the sample card if the measure of quality does not meet a threshold level. In some embodiments, instructions to the user to prepare a new sample card includes specific directions for the user to correct previous errors in preparing the sample card (e.g., apply more or less sample to the sample application zone; change the length of incubation or waiting period after applying the sample to the sample application zone). In some embodiments, act 112 comprises providing instructions to the user to proceed with downstream analyses (e.g., downstream in vitro analyses, e.g., diagnostic testing of the serum zone to determine levels of a specific biological analyte in the biological sample). In some embodiments, act 112 comprises providing instructions to the user to deliver the sample card to a third-party (e.g., a third-party laboratory, e.g., third-party shipping service).

After instructions are provided to the user, process 100 proceeds to act 113 for a user to proceed in accordance with the instructions. For example, act 113 involves a user preparing a new sample card comprising a new biological sample for the subject in response to instructions in act 112 to prepare new sample card comprising a new biological sample for the subject. The user in act 113 may be the same or different user who received the instructions in act 112.

The above described process 100 is illustrative of the methods of the disclosure and it should be appreciated that variations of process 100 are possible. For example, one or more acts of process 100 may be omitted, in some embodiments.

For example, in some embodiments, acts 102 and 113 may be omitted. For example, in some embodiments, act 102 may be performed prior to the commencement of performance of process 100. For instance, process 100 may commence after a sample card has been prepared by a first user (e.g., a patient) and delivered to a second user (e.g., a technician at a third-party laboratory). In some embodiments, act 104 may also be omitted. For instance, process 100 may commence after a sample card has been prepared and an image of said sample card has been captured by a first user (e.g., a patient) and the image (and optionally the sample card) are delivered to a second user (e.g., a technician at a third-party laboratory).

For example, in some embodiments, acts 112 and 113 are omitted. For example, process 100 may be completed following the determination of a measure of quality of the sample card and/or the image of the sample card. For instance, process 100 may be completed after a measure of quality has been determined and that measure of quality is provided to a user who performs an independent assessment of said measure of quality.

For example, in some embodiments, any one of acts 104-112 are omitted. For example, in some embodiments, acts 108-112 are omitted when the location of the serum zone cannot be determined (e.g., because the size of the sample flow zone does not exceed a threshold level).

FIG. 2 is a flowchart illustrating an example implementation of act 104 of process 100 shown in FIG. 1, according to some embodiments of the technology described herein. Act 104 includes capturing an image of the sample card comprising a biological sample. In some embodiments, as described herein above including with respect to FIG. 1, the image of the sample card can be captured using a variety of methodologies.

As shown, the example implementation of act 104 begins at act 104 for the initial capture of an image of the sample card. Following the initial capture of the image, the subsequent acts of the example implementation of act 104 are performed by a computing device. Following the initial capture of the image, the example implementation of act 104 proceeds to act 202 for determining whether the image is clear and in focus. In some embodiments, an image that is clear and in focus contains edges and text that are visually distinguishable. In some embodiments, an image that is not clear and is not in focus contains edges and text that are not visually distinguishable. If the image is not clear and not in focus, then the computing device will proceed to act 212 for providing instructions (e.g., to the camera or scanner; or to a user) to capture a new image of the sample card. In such embodiments, these instructions to capture a new image of the sample card function as the completion of process 100. If the image is clear and in focus, then the example implementation of act 104 will proceed to act 204.

Act 204 involves the identification of the largest contours in the image as representing the candidate dimensions of the sample card. The largest contours are presumed to represent the dimensions (or boundaries) of the sample card. In some embodiments, the largest contours in the image are a uniform shape (e.g., a rectangle). In some embodiments, the largest contours are identified using an edge detection technique (e.g., Canny edge detection).

Following the identification of the largest contours in the image (e.g., candidate dimensions of the sample card) in act 204, the example implementation of act 104 proceeds to act 206 for determining whether the candidate dimensions meet or exceed the size threshold for downstream processing. For example, in some embodiments, the size threshold for downstream processes relates to the height, width or area of the candidate dimensions of the sample card in relation to the image. For example, in some embodiments, the height or the width of the candidate dimensions of the sample card should be at least 40% (e.g., at least 50% or 60%) of the total image. In some embodiments, the total area within the candidate dimensions of the sample card should represent at least 40% (e.g., at least 50% or 60%) of the total area of the image. If the candidate dimensions do not meet or exceed the size threshold for downstream processing (e.g., total area within the candidate dimensions of the sample card is at least 40% of the total area of the image), then the computing device will proceed to act 212 for providing instructions (e.g., to the camera or scanner; or to a user) to capture a new image of the sample card. If the candidate dimensions meet or exceed the size threshold for downstream processing, then the example implementation of act 104 will proceed to act 208.

Following act 206, the example implementation of act 104 proceeds to act 208 for determining whether the candidate dimensions are approximately the expected aspect ratio (e.g., ratio of length relative to width) of the actual sample card. For example, in some embodiments, the identity of the sample card is known (e.g., manufacturer and model of the sample card is known) such that the actual aspect ratio of the sample card is known. Thus, in some embodiments, the aspect ratio of the largest contours in the image (e.g., candidate dimensions of the sample card) can be compared to the expected aspect ratio of the sample card. If the aspect ratio of the largest contours in the image are not approximately the same as the expected aspect ratio, then the computing device will proceed to act 212 for providing instructions (e.g., to the camera or scanner; or to a user) to capture a new image of the sample card. In such embodiments, these instructions to capture a new image of the sample card function as the completion of process 100. If the aspect ratio of the largest contours in the image are approximately the same as the expected aspect ratio, then the example implementation of act 104 will proceed to act 210. Act 210 involves proceeding with the captured image for the completion of the example implementation of act 104.

FIG. 3A is a flowchart illustrating an example implementation of act 106 of process 100 shown in FIG. 1, according to some embodiments of the technology described herein. Act 106 includes determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique. In some embodiments, as described herein above including with respect to FIG. 1, the location of the serum zone can be determined using a variety of image processing techniques.

As shown, the example implementation of act 106 begins at act 302 for determining a location of a sample application zone and/or a sample flow zone. A sample application zone is as described in the section of this disclosure entitled “Sample Application Zone” and elsewhere in this disclosure. A sample flow zone is as described in the section of this disclosure entitled “Sample Flow Zone” and elsewhere in this disclosure. The location of the sample application zone and/or a sample flow zone may be determined using an edge detection technique as described herein. In some embodiments, the location of a sample application zone is determined using an edge detection technique (e.g., Canny edge detection). In some embodiments, the location of a sample flow zone is determined using an edge detection technique (e.g., Canny edge detection). In some embodiments, the location of a sample application zone and a sample flow zone are determined using an edge detection technique (e.g., Canny edge detection). Techniques for determining a location of a sample application zone and/or a sample flow zone in the sample card are described herein including with respect to FIG. 3B.

After determining a location of a sample application zone and/or a sample flow zone in act 302, the example implementation of act 106 proceeds to act 304 for determining at least one characteristic of the sample application zone and/or a sample flow zone based on the identified location(s) of the sample application zone and/or a sample flow zone. For example, act 304 may comprise determining the color of the sample within the sample application zone, the percentage of area within the sample application zone that contains sample, color of the sample within the sample flow zone and/or the total area of the sample flow zone. In some embodiments, determining the color of the sample within the sample application zone involves determining the level of opacity of the sample application zone. In some embodiments, determining the color of the sample application zone involves determining the average color across the entire area of the sample application zone. The sample application zone may be uniformly colored across its entire area or contain different shades or levels of opacity across its area. In some embodiments, determining the percentage of area within the sample application zone that contains sample involves visually determining the area of sample application zone that contains sample based on the color of the sample application zone and dividing that area by the total area within the dimensions of the sample application zone. In some embodiments, determining the color of the sample flow zone involves determining the average color across the entire area of the sample flow zone. The sample flow zone may be uniformly colored across its entire area or contain different shades or levels of opacity across its area. In some embodiments, determining the total area of the sample flow zone involves calculating the area within the dimensions of the sample flow zone. In some embodiments, the total area of the sample flow zone is at least 100 mm² (e.g., at least 100, 125, 150, 175, 200, 225, 250, 300, 400 or 500 mm²).

After determining at least one characteristic of the sample application zone and/or a sample flow zone in act 304, the example implementation of act 106 proceeds to act 306 for determining a location of a serum region-of-interest (serum RO I) comprising a serum zone from the sample card based on the location(s) and characteristic(s) of the sample application zone and/or sample flow zone. A serum ROI comprises a serum zone, but does not comprise the sample application zone or the sample flow zone. Furthermore, in some embodiments, the dimensions of the serum ROI and the serum zone cannot be detected using an edge detection technique. In some embodiments, the location of the serum ROI is determined based on the edges of the sample flow zone. For example, in some embodiments, the serum ROI extends from the immediate end of the sample flow zone, wherein the sample flow zone is positioned between the serum ROI and the sample application zone. In some embodiments, the location of the serum ROI is determined based on the edges of the sample application zone. For example, in some embodiments, the serum ROI begins at a fixed distance and/or position relative to the sample application zone. In some embodiments, the end of the serum ROI that is farthest from the sample application zone is located at a fixed location on the sample card. In some embodiments, the serum ROI has a width that is a fixed number of pixels less than the width of the sample card or has a width that is a fixed percentage of the width of the sample card. In some embodiments, the serum ROI is detected based on a known absolute location on the sample card. In some embodiments, the serum ROI is extracted from the image. In some embodiments, act 306 is omitted and act 106 proceeds directly to act 308 following act 304.

After determining a location of the serum ROI in act 306, the example implementation of act 106 proceeds to act 308 for determining a location of the serum zone by applying a segmentation technique to the serum ROI. A segmentation technique may be any segmentation technique known to skilled person. For example, a segmentation technique may be a watershed segmentation technique, a deformable model (e.g., an active shape or contour model, a deformable model based on level sets), or a texture analysis. Techniques for determining a location of the serum zone are described herein including with respect to FIG. 3C.

FIG. 3B is a flowchart illustrating an example implementation of act 302 shown in FIG. 3A, according to some embodiments of the technology described herein. Act 302 includes determining a location of a sample application zone and/or a sample flow zone. In some embodiments, as described herein above including with respect to FIG. 1, the location(s) of a sample application zone and/or a sample flow zone can be determined using a variety of techniques.

As shown, the example implementation of act 302 begins at block 310. Block 310 comprises act 310a for obtaining an image of a sample card; and act 310b for pre-processing of the image of the sample card. In some embodiments, act 310b is omitted. In some embodiments, act 310a involves capturing an image of the sample card using a technique as described herein. In some embodiments, act 310a involves obtaining a copy of an image of a sample card that had been previously captured and stored on a computing device. In some embodiments, act 310b involves extraction of the dimensions of a sample card from the remainder of the image. In some embodiments, act 310b involves flattening the image of the sample card. In some embodiments, act 310b involves re-sizing the image of the sample card to a preset size and/or shape. Re-sizing the image of the sample card to a preset size and/or shape may have the effect of providing a more robust performance (e.g., of act 302).

After block 310, the example implementation of act 302 proceeds to act 312 for detecting edges of the sample card in the image. In some embodiments, act 312 involves using any edge detection technique known to a skilled person (e.g., Canny edge detection). In some embodiments, act 312 involves the detection of all edges contained within the image, including the edges corresponding to the dimensions or boundaries of the sample card and all edges within the dimensions of the sample card.

After detecting edges of the sample card in the image in act 312, the example implementation of act 302 may proceed to act 314 for dilating and eroding detected edges. Act 314 is optional in the example implementation of act 302. If act 314 is omitted, then the example implementation of act 302 proceeds to act 316.

Dilating the detected edges may involve transforming the image of the detected edges such that the detected edges maintain the same dimensions as the original, but are a different size. In some embodiments, dilating detected edges involves increasing the thickness of the detected edges (e.g., by adding pixels to the boundaries of the edges). Eroding the detected edges involves removing the boundaries of regions of foreground pixels surrounding the detected edges. In some embodiments, the combination of dilation and erosion increases the definition of the detected edges. A skilled person understands how to dilate and erode detected edges using conventional means.

The example implementation of act 302 proceeds to act 316 for identifying, from among the detected edges, the edges of the sample application zone within a region-of-interest. In some embodiments, identifying the edges of the sample application zone involves locating edges having an expected shape and size within the dimensions of the sample card. In some embodiments, a sample application zone has an expected shape and size if the manufacturer and/or model of the sample card is known. Further, in some embodiments, the expected location of the edges of the sample application zone is known or fixed based on the knowledge of the manufacturer or model of the sample card. In some embodiments, the edges of the sample application zone are located dynamically. In some embodiments, the edges of the sample application zone are located dynamically by identifying the largest regular shape (e.g., perfect square or perfect rectangle) within the dimensions of the sample card.

After identifying the edges of the sample application zone in act 316, the example implementation of act 302 proceeds to act 318 for identifying edges of the sample flow zone based on the identified edges of the sample application zone. In some embodiments, act 318 involves identifying edges of the sample flow zone based on the expected location of a first edge of the sample flow zone relative to the sample application zone. In some embodiments, the expected location of the edges of the sample flow zone relative to the sample application zone is known or fixed based on the knowledge of the manufacturer or model of the sample card. In some embodiments, the first edge is the edge of the sample flow zone that is closest to the sample application zone. The remaining edges of the sample flow zone (e.g., all edges except the first edge) may be identified by being attached to the first edge. In some embodiments, the sample flow zone is an irregular shape. In some embodiments, the sample flow zone is the largest irregular shape within the dimensions of the sample card. In some embodiments, act 316 is omitted and act 302 proceeds directly to act 318 following act 312 or act 314. After identifying the edges of the sample flow zone in act 318, the example implementation of act 302 proceeds to act 320 to confirm whether the sample application zone and the sample flow zone were both identified. In some embodiments, confirming that the sample application zone and the sample flow zone were both identified involves identifying edges that correspond to both zones. If both of the sample application zone and the sample flow zone were not identified (e.g., edges corresponding to both zones were not identified), then the computing device will proceed to act 324 for providing instructions (e.g., to a camera or scanner; or to a user) to capture a new image of the sample card. If both of the sample application zone and the sample flow zone were identified (e.g., edges corresponding to both zones were identified), then the example implementation of act 302 proceeds to act 322.

Act 322 involves determining whether the size of the sample flow zone meets or exceeds a threshold level. In some embodiments, the threshold level for the size of the sample flow zone is determined by the manufacturer or model of a sample card. For example, an ADx 100 sample card comprises a line indicator (see, the line of FIG. 4 labeled with “Add blood drops until the strip turns red to this line”) that functions to set the threshold level. In some embodiments, the threshold level of the size of the sample flow zone is determined based on its size relative to the sample application zone. For example, in some embodiments, the threshold level requires that the total area of the sample flow zone is at least 50% of the total area of the sample application zone. If the size of the sample flow zone does not meet or exceed a threshold level, then the computing device will proceed to act 326 for providing instructions to obtain a new biological sample on an unused sample card.

FIG. 3C is a flowchart illustrating an example implementation of act 308 shown in FIG. 3A, according to some embodiments of the technology described herein. Act 308 includes determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique. In some embodiments, as described herein above including with respect to FIG. 1, the location of the serum zone can be determined using a variety of image processing techniques.

As shown, the example implementation of act 308 begins at act 328 for determining seed points for the serum zone and the background of the sample card. In some embodiments, the seed points for the serum zone are preset or fixed within the sample card. In some embodiments, the seed points for the background are preset or fixed within the sample card. In some embodiments, the seed points for the serum zone are located in a fixed position and distance from the edge of the sample flow zone. In some embodiments, the seed points for the serum zone are located in a fixed position and distance from the edge of the serum region-of-interest. In some embodiments, the seed points for the serum zone are located in a fixed position from the edge of the sample flow zone by a distance that is equal to the height of the sample flow zone. In some embodiments, the seed points for the serum zone are located in a fixed position from the edge of the serum region-of-interest by a distance that is equal to the height of the sample flow zone. In some embodiments, the seed points are dynamically determined. Dynamic determination of seed points for the serum zone and background may be performed by visually analyzing the relative color or opacity of a serum region-of-interest (e.g., that does not contain the sample application zone or the sample flow zone) and selecting sets of locations that are most distinct from one another. The background of the sample card for purposes of determining seed points may comprise a region within the dimensions of the sample card that do not comprise any change in color or opacity following application of the sample. In some embodiments, the background of the sample card is located at a fixed distance away from the sample application zone (e.g., at the edge of the serum ROI that is farthest from the sample application zone)a.

After determining seed points for the serum zone and the background of the sample card in act 328, the example implementation of act 308 proceeds to act 330 for detecting an outline of the serum zone using the seed points for the serum zone and background in a segmentation technique. A segmentation technique may be any segmentation technique known to a skilled person. For example, a segmentation technique may be a watershed segmentation technique, a deformable model (e.g., an active shape or contour model, a deformable model based on level sets), or a texture analysis. In some embodiments, the seed point(s) for the serum zone and/or the background are utilized to initiate the segmentation technique. In some embodiments, act 330 involves identifying the outline or boundary of the serum zone based on the intensities of pixels in the serum zone relative to the intensities of pixels in the background of the sample card.

After determining the outline of the serum zone in act 330, the example implementation of act 308 proceeds to act 332 for dilating and eroding detected edges. Act 332 is optional in the example implementation of act 308. If act 332 is omitted, then the example implementation of act 308 proceeds to act 334.

Dilating the detected edges may involve transforming the image of the detected edges such that the detected edges maintain the same dimensions as the original, but are a different size. In some embodiments, dilating detected edges involves increasing the thickness of the detected edges (e.g., by adding pixels to the boundaries of the edges). Eroding the detected edges involves removing the boundaries of regions of foreground pixels surrounding the detected edges. In some embodiments, the combination of dilation and erosion increases the definition of the detected edges. A skilled person understands how to dilate and erode detected edges using conventional means. The example implementation of act 302 proceeds to act 334 for determining the dimensions of the serum zone. In some embodiments, act 334 involves calculating the total area of the serum zone. In some embodiments, act 334 involves determining the shape of the serum zone. In some embodiments, act 334 involves determining the dimensions of the largest regular shape (e.g., square or rectangle) within the serum zone.

In some embodiments, a method of evaluating the quality of a sample card comprising a biological sample and/or the image of the sample card is provided in FIG. 5. To prepare a sample card, a user lances their finger and places one or multiple drops of blood into the indicated window (corresponding to the sample application zone). The user is then instructed to wait a period of time (e.g., 30 minutes) for the blood to flow through the rest of the sample card and dry. While the sample is wicked to the other side, the serum is separated from the hematocrit. Once dry, the user can then use their smartphone (or any other portable computing device) and evaluate the quality of the sample card comprising a biological sample and/or an image of the sample card using a method of the disclosure. As shown in FIG. 5, which is a nonlimiting example method of the disclosure, the input image of the sample card is first subjected to edge detection (e.g., Canny Edge detection). The largest shape (rectangle) is determined to provide the dimensions of the sample card. At this stage, image processing techniques to check for correct card aspect ratio and to flatten the card back to a perfect shape (rectangle or square) can optionally be performed. Following this, edge detection is performed together with optional dilation and erosion to clean up the detected edges; and a location of the sample application zone is determined by identifying the largest shape confined within a predetermined region-of-interest in the sample card. A location of the sample flow zone can be determined using the edge detection results and the known relative position of the sample flow zone from the sample application zone on an ADx 100 sample card. A serum region-of-interest (serum ROI) is extracted based on the known relative position of the serum ROI from the sample flow zone; and preset seed points for serum (such as a vertical line near bottom) and background (such as a horizontal line or lines near top) are selected for initiation of a segmentation algorithm (e.g., watershed segmentation algorithm) to detect the serum zone. At least one characteristic of the serum zone can then be determined based on the identified location of the serum zone, which enables determination of a measure of the quality of the sample card and/or the image of the sample card.

Preparing a sample card A sample card for use in the disclosure may be capable of separating, purifying or isolating specific components of a biological sample from other components. For example, in some embodiments, a sample card is capable of separating blood cells (e.g., red blood cells) from serum in a blood sample from a user. In some embodiments, a sample card comprises filter paper (e.g., cellulose filter paper). In some embodiments, a sample card is a dried blood spot card or a dried plasma spot card.

A sample card may be a sample card produced by any manufacturer or supplier known to a person of ordinary skill in the art. For example, a sample card may be produced by Advance Dx, Whatman®, Novilytic, or Roche. In some embodiments, a sample card is an Advance Dx 100 (ADx 100) sample card, a Whatman® protein saver card, a cobas® Plasma Separation Card, or a Noviplex™ Plasma Prep Card. A sample card may be as described in U.S. Patent 8,062,608.

In some embodiments, a sample card is any reasonable shape or size and comprises any reasonable dimensions. For example, in some embodiments, the sample card is a rectangle or a square. In some embodiments, a sample card having a rectangular shape can have a length of 5- 50 cm, 5-40 cm, 5-30 cm, 5-25 cm, 5-20 cm, 5-15 cm, 10-20 cm, or 6-20 cm. In some embodiments, a sample card having a rectangular shape can have a width of 1-30 cm, 1-20 cm, 1-15 cm, 2-15 cm, 2-10 cm, 2-5 cm, 3-8 cm, or 3-6 cm. In some embodiments, a sample card having a square shape can have a length of 5-50 cm, 5-40 cm, 5-30 cm, 5-25 cm, 5-20 cm, 5-15 cm, 10-20 cm, 6-20 cm, 1-30 cm, 1-20 cm, 1-15 cm, 2-15 cm, 2-10 cm, 2-5 cm, 3-8 cm, or 3-6 cm.

In some embodiments, a sample card has the dimensions of a sample card used or produced by a known sample card manufacturer. In some embodiments, a sample card has the dimensions of an Advance Dx 100 (ADx 100) sample card, a Whatman® protein saver card, a cobas® Plasma Separation Card, or a Noviplex™ Plasma Prep Cards. The dimensions of the sample card may, in some embodiments, allow for identification of the brand, manufacturer, and model of the sample card (e.g., following edge detection to identify the edges of the sample card).

A sample card may comprise one or more distinct zones or regions. For example, a sample card used in the disclosure may comprise a sample application zone, a sample flow zone and a serum zone. The sample flow zone may be positioned between a sample application zone and the serum zone. In some embodiments, the sample application zone is located in a fixed location of the sample card. A sample card may be prepared for use in the methods of the disclosure following application of a biological sample (e.g., a liquid sample, e.g., blood) to the sample application zone. A sample card can contain any amount of identifying information on the sample card, provided that the identifying information does not materially affect the analysis of the sample application, sample flow, and plasma domains. Identifying information can be related to the manufacturer of the sample card (e.g., brand, model, lot and/or serial number). Identifying information can be related to the user and could include the name of the user, date of birth of the user, identification numbers of the user (e.g., unique identifier in laboratory site’s system, driver’s license) and/or the date of collection of the biological sample.

Determining a location of a distinct zone of a sample card

Sample application zone

The sample application zone is a distinct zone of a sample card that is intended for the application (or input) of the biological sample. The biological sample is applied to the sample application zone and then the components of the sample may “flow” or travel away (e.g., by capillary action or wicking) from the sample application zone. In some embodiments, the sample application zone performs an initial filtering of the biological sample before the primary components of the biological sample are allowed to flow out of the sample application zone.

A location of the sample application zone can refer to the position, shape, and/or size of the sample application zone. A location of the sample application zone can be determined by identifying the edges of the largest shape (e.g., rectangle or square) contained within a predetermined region-of-interest in the sample card. In some embodiments, a location of the sample application can be determined by identifying a shape having a size and dimensions that correspond to the size and dimensions of the expected sample application zone of a known sample card. For example, the location (e.g., position, shape and size) of the sample application zone belonging to an ADx 100 sample card can be determined based on its fixed location within the ADx 100 sample card. See, e.g., FIG. 4.

The sample application zone can be identified and its location determined by performing an edge detection technique of a sample card (e.g., a sample card comprising a biological sample). As shown in FIG. 5 (fifth panel, top), the edge detection technique can identify the sample application zone, which commonly has a fixed location on the sample card, and/or a regular shape (e.g., a rectangle or square), and/or is the largest shape contained within a predetermined region-of-interest in the sample card. In the example shown in FIG. 5 (fifth panel, top), the sample application zone has a fixed location with the ADx 100 sample card used, a regular shape, and is the largest shape contained within its general expected location (region- of-interest) in the sample card. Following determination of the location of the sample application zone, different aspects of the method can be utilized to determine at least one characteristic of the sample application zone based on the identified location of the sample application zone. The at least one characteristic of the sample application zone may be the color of the sample within the sample application zone and/or the percentage of area within the sample application zone that contains sample. If the sample is a blood sample, then the color within the sample application zone is anticipated to be red. If the color within the sample application zone for a blood sample is not red, then there is potentially an issue with the quality of the blood sample. Furthermore, in some embodiments, the color of the sample application zone can function as an additional confirmation that the zone has been correctly identified using the method (e.g., correctly identified by the portable computing device). The percentage of area within the sample application zone that contains sample can provide an indication of whether or not there was a threshold quantity of sample loaded to the sample card. If the percentage of area within the sample application zone that contains sample is low (e.g., less than 90%, 80%, 70%, 60%, 50%, 40%, or 30% of the total area of the sample application zone contains sample), then there is potentially an issue with the quality or quantity of the sample. This could indicate that a new sample card comprising a new biological sample is necessary.

Sample flow zone

The sample flow zone is a distinct zone of the sample card that may include cells and cell components (e.g., lipid membranes). As the biological sample flows away from the sample application zone (e.g., by capillary action or wicking), the sample flow zone is formed. In sample cards comprising a blood sample, the sample flow zone comprises red blood cells and optionally white blood cells, which can be visually observed based on the color of the zone.

A location of the sample flow zone can refer to the position, shape, and/or size of the sample flow zone. A location of the sample flow zone can be identified and its location determined by performing an edge detection technique of a sample card (e.g., a sample card comprising a biological sample). Following identification of the sample application zone, the location of the sample flow zone can be determined based on the location of the sample application zone and known information (e.g., manufacturer of the sample card, dimensions of the sample card) that indicate a location of the sample flow zone relative to the sample application zone. In some embodiments, the sample flow zone is identified by looking within a certain region-of-interest that is preset at a fixed relative position from the identified sample application zone. For example, as shown in the example of FIG. 5 (first panel, bottom), the edge detection technique can identify the sample flow zone based on the distance between the sample application zone and the edge of the sample flow zone nearest to that sample application zone. The shape and size of the sample flow zone may, in some embodiments, be determined by determination of the extent of the color associated with the sample flow zone. For example, if the sample is a blood sample, then a shape that is positioned with a certain region-of-interest that is preset at a fixed relative position from the identified sample application zone that contains a red color could provide information about the location of the sample flow zone.

In some embodiments, the sample card comprising a biological sample does not comprise a sample flow zone. In such embodiments, the sample card may be positioned such that the sample flows vertically (e.g., instead of horizontally) such that there is no sample flow zone. Alternatively, elements of the card may cover the sample flow zone, making it not visible to the user (nor visible within the obtained image of the sample card).

Following determination of a location of the sample flow zone, different aspects of the method can be utilized to determine at least one characteristic of the sample flow zone. The at least one characteristic of the sample flow zone may be the color of the sample within the sample flow zone and/or the total area of the sample flow zone. If the sample is a blood sample, then the color within the sample flow zone is anticipated to be red. If the color within the sample flow zone for a blood sample is not red, then there is potentially an issue with the quality of the blood sample. Furthermore, in some embodiments, the color of the sample flow zone can function as an additional confirmation that the zone has been correctly identified using the method (e.g., correctly identified by the portable computing device). The total area of the sample flow zone can provide an indication of whether there was a threshold quantity of sample loaded to the sample card. If the total area of the sample flow zone is low, then there is potentially an issue with the quality of the sample (e.g., not enough sample was deposited on the sample card, e.g., by a user). If the total area of the sample flow zone is high, then there is potentially an issue with the quality of the sample (e.g., too much sample was deposited on the sample card, e.g., by a user). Too much sample can overload the sample card with a larger than desired sample flow zone, which can have the effect of decreasing the available space on the sample card for the serum zone.

Serum zone

The serum zone (also referred to as a “plasma zone”) is a distinct zone of the sample card that comprises ions, proteins, dissolved gases, and/or nutrient molecules from the biological sample; and extends beyond the sample flow and sample application zones. In embodiments lacking a sample flow zone, the serum zone may be located at a fixed position relative to the sample application zone. In embodiments including a sample flow zone, the sample flow zone is positioned between the sample application zone and the serum zone. In some embodiments, the serum zone is substantially devoid of cells (e.g., red blood cells). In some embodiments, the serum zone is devoid of red blood cells. In some embodiments, the serum zone contains serum, plasma or other fluidic elements, but does not contain substantial amount of cells, that are found within a biological sample. In some embodiments, the serum zone comprises serum or plasma from the biological sample that has been separated or isolated from the red blood cells of the biological sample. In some embodiments, the serum zone, or plasma zone, comprises serum or plasma from the biological sample that has been separated or isolated from the majority of cells (e.g., red blood cells) of the biological sample.

A location of the serum zone can refer to the position, shape, and/or size of the sample flow zone. A location of the serum zone can be identified and its location determined by first detecting (and optionally extracting) a serum region-of-interest (serum ROI). In embodiments including a sample flow zone, the serum ROI is detected based on the location of the sample flow zone. In such embodiments, the serum ROI begins immediately after the end of the sample flow zone (on the side of the sample flow zone most distant from the sample application zone), ends at a fixed location on the sample card, and has width that is a fixed number of pixels less than the width of the sample flow zone or has width that is a fixed percentage of the width of the sample flow zone or sample card.

In embodiments that do not include a sample flow zone, the serum ROI is detected based on the location of the sample application zone. In such embodiments, the serum ROI begins at a fixed distance or position relative to the sample application zone, ends at a fixed location on the sample card, and optionally has width that is a fixed number of pixels less than the width of the sample application zone or has width that is a fixed percentage of the width of the sample flow zone or sample card.

Following detection and optional extraction of the serum ROI, a location of the serum zone within the serum ROI is determined using a segmentation technique (e.g., watershed segmentation technique). Performance of the segmentation technique within the confines of the identified serum ROI (and not the broader sample card) was found by the inventors to simplify the computing task, to make the identification of the plasma more robust, and increase efficiency (e.g., efficiency of time). The segmentation technique is performed, in some embodiments, by utilizing seed points (e.g., preset seed points) for the serum zone and the background. The seed points for the serum zone, which initiate the performance of the segmentation technique, may be positioned from the edge of the sample flow zone or serum ROI by a distance that is equal to the height of the sample flow zone. In some embodiments, the seed points for the serum zone are positioned from the edge of the sample flow zone or serum ROI by a distance that is equal to about 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%, 105%, 110%, 115%, 120%, 130%, 140%, or 150% of the height of the sample flow zone. In some embodiments, the seed points for the serum zone constitute a line that starts a certain fixed distance from the edge of the sample flow zone or serum ROI, and ends at a location that is at a certain relative distance from the edge of the sample flow zone or serum ROI as described above. In some embodiments, the serum zone has a height that is larger than the height of the sample flow zone.

In other embodiments, the preset seed points for the serum zone are positioned in a fixed location relative to the location of the sample flow zone or the serum ROI. The preset seed points for the background are similarly positioned in a fixed location relative to the location of the sample flow zone or the serum ROI, in some embodiments. In yet other embodiments, the preset seed points for the serum zone are positioned in a fixed location relative to the dimensions of the sample card and/or the position of the sample application zone and/or the position of the sample flow zone and/or the position of the serum ROI. The fixed location may be 0-25, 1-15, 1-12, 1-10, 2-15, 2-10, 3-10, 3-7, or 5-10 mm from the upper edge of the sample flow zone or lower edge of the serum region-of-interest and/or equidistant from the side edges of the serum region-of-interest and/or parallel to the side edges of the serum region-of-interest. In other embodiments, the preset seed points for the background are positioned in a fixed location relative to the dimensions of the sample card and/or the position of the sample application zone and/or the position of the sample flow zone and/or the position of the serum ROI. The fixed location may be 0-10 mm from the upper edge of the serum ROI and/or equidistant from the side edges of the serum ROI and/or perpendicular to the side edges of the serum ROI.

Following determination of a location of the serum zone, different aspects of the method can be utilized to determine at least one characteristic of the serum zone. The at least one characteristic of the serum zone may comprise color of the serum zone, total area of the serum zone, and/or number of discrete shapes within the serum zone. In some embodiments, it is expected that the color within the serum zone will be the same or similar to the color of the background (e.g., the color of the sample card outside of the sample application, sample flow and serum zones). In some embodiments, if the color within the serum zone is highly opaque or lacking transparency, then the measure of quality of the sample card is low. Specifically, if the biological sample is blood, the color of the serum zone can indicate the amount of residual blood cells present within the serum zone. The total area of the serum zone can provide an indication of the measure of quality of the sample card or biological sample applied to it. In some embodiments, if the total area of the serum zone is low, then the measure of quality of the sample card is low (e.g., not enough sample was deposited on the sample card, e.g., by a user). In some embodiments, the total area of the serum zone exceeds a threshold level and enables one or more in vitro analyses to be performed on the sample card. In order to perform one or more in vitro analyses, a technician or user needs to be able to remove one or more discrete segments of the serum zone, which can be optimized by maximizing the total number of discrete shapes that are contained within the serum zone. In some embodiments, the discrete shapes within the serum zone are squares (e.g., 10x10 mm squares), rectangles, or circles. In some embodiments, if the total number of discrete shapes is low (e.g., one or fewer discrete shapes having a desired size), then the measure of quality of the sample card is low.

The measure of quality of a sample card comprising a biological sample and/or the image of the sample card can refer to the quality of the physical sample card (e.g., the sample card itself) or the quality of the image. Determining the quality of the sample card may include a determination relating to the performance of the sample card (e.g., presence or absence of manufacturing defects of the card) or the technique of the user in preparing the sample card. Following a determination of the measure of quality of the sample card, in some embodiments, the portable computing device can provide instructions to the user to prepare a new sample card if the quality of the sample card is inadequate, wherein said instructions can include directions for the user to correct previous errors in technique. In other embodiments, if the quality of the sample is adequate, then the portable computing device can instruct the user that the sample card is acceptable for downstream analysis (e.g., at a laboratory). In some embodiments, the quality of a sample card and/or the image of the sample card can refer to the quality of an image of the sample card. In some embodiments, if the image of the sample card is inadequate, then the portable computing device can provide instructions to the user to obtain a new image of the same sample card or, if scanning, bring the camera closer or at a different angle relative to the sample card.

In some embodiments, the term “measure of quality of the sample card” is synonymous with the term “validity of the sample card.”

Techniques of the user that can result in a sample card lacking adequate quality include the application of too much or too little sample to the sample application zone by the user; improper speed used to prepare the sample; extensive wicking or capillary action of the hematocrit such that the hematocrit extends far enough down the sample card to affect the content of the dried serum or plasma; extensive sample hemolysis such that the sample card is unable to separate red blood cells effectively; or overflow of the sample outside of the confines of the sample application zone.

In some embodiments, instructions provided to the user from the at least one portable computing device comprise: (i) instructions to obtain a new biological sample on an unused sample card based on the measure of quality, (ii) instructions to deliver the sample card to a laboratory for analysis of the biological sample, or (iii) instructions to analyze the biological sample. In some embodiments, an individual user is performing the acts of the method (e.g., with a portable computing device). In other embodiments, a laboratory site is performing the acts of the method (e.g., with a portable computing device). A new sample card may be prepared based on instructions from the portable computing device. In some embodiments, a new sample card comprises a new liquid biological sample from the same user.

Image processing

Methods of the disclosure involve one or more image processing techniques to determine the locations of the distinct zones within the sample card and/or the dimensions of the sample card. Edge detection can be useful for determining the dimensions of a sample card and/or identifying the sample application zone, sample flow zone and/or serum zone. Segmentation techniques can be useful for determining a location of the serum zone. Individual image processing techniques are conventional and known to a skilled person. In some embodiments, the image processing techniques for use in the methods described herein may utilize algorithms implemented in libraries such as Open Source Computer Vision Library (OpenCV) and/or any other suitable software libraries.

Edge detection techniques function to identify edges and curves in an image of a sample card comprising a biological sample. In some embodiments, an edge detection technique identifies edges and curves by detecting changes in brightness or color within the image (e.g., abrupt changes in brightness or color). An edge detection technique comprises, in some embodiments, a Canny edge detection, a Hough transform, Sobel edge detection, Prewitt edge detection, and/or Laplacian edge detection. In some embodiments, an edge detection technique comprises a Canny edge detection.

An edge detection technique or technique may comprise additional processing acts. For example, in some embodiments, an edge detection technique or technique further comprises erosion or dilation of the identified edges. In some embodiments, edge detection technique or technique further comprises feature extraction.

An image of a sample card may be warped using an image analysis technique that flattens the dimensions of the sample card into a perfect shape (e.g., a perfect rectangle or square). This image flattening can assist to simplify downstream analysis of the image and may be performed at any act in the methods of the disclosure. In some embodiments, the image or a part of it is flattened into a perfect shape following an initial edge detection. A segmentation technique can be applied to the image using seeds (e.g., preset seeds). These techniques function to identify distinct zones (e.g., serum zone) of the sample card by expanding areas out from seeds for the serum zone and seeds for the background until those areas meet one another (e.g., to form the interface between the serum zone and background). The segmentation technique may be a watershed segmentation technique, a deformable model, or a texture analysis. In some embodiments, a deformable model is an active shape or contour model, or a deformable model based on level sets. In some embodiments, the segmentation technique does not require seed points in order to determine the serum zone and background.

The seed points (e.g., preset seed points) for the serum zone may be positioned from the edge of the sample flow zone or serum ROI by a distance that is equal to the height of the sample flow zone. In some embodiments, the seed points for the serum zone are positioned from the edge of the sample flow zone or serum ROI by a distance that is equal to about 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%, 105%, 110%, 115% 120%, 130%, 140%, or 150% of the height of the sample flow zone. In some embodiments, the serum zone has a height that is larger than the height of the sample flow zone. The background seed points are typically located in the background of the sample card on the opposite end of the sample card relative to the sample application zone. In some embodiments, the seed points for the serum zone constitute a line that starts a certain fixed distance from the edge of the sample flow zone or serum ROI, and ends at a location that is at a certain relative distance from the edge of the sample flow zone or serum ROI as described above.

Computing devices, cameras, and image capture

A person of ordinary skill in the art will recognize that the methods of the disclosure can utilize any camera to obtain an image of the sample card comprising a biological sample and can utilize any computing device (e.g., portable computing device). In some embodiments, a portable computing device is a mobile computing device, computing tablet, smartphone, or laptop. In some embodiments, a fixed computing device (e.g., a desktop computer) is equivalent with a portable computing device with respect to its capabilities to perform the methods of the disclosure.

In some embodiments, the camera used to capture the image of the sample card is a camera belonging to the portable computing device (e.g., a smartphone camera) or a digital camera. In other embodiments, the camera used to capture the image of the sample card is a scanner communicatively coupled to the portable computing device. A scanner that is communicatively coupled to the portable computing device may be directly coupled to the computing device (e.g., through a cable) or may be coupled to the computing device over a wireless internet connection or through cloud computing. Capturing an image of the sample card may comprise capturing a still image or photograph; or continuous scanning (e.g., continuous scanning by the camera belonging to the portable computing device or the scanner communicatively coupled to the portable computing device). In some embodiments, continuous scanning is performed using a document scanning application (e.g., a document scanning application on a smartphone).

Computational Systems

An illustrative implementation of a computer system 600 that may be used in connection with any of the embodiments of the technology described herein (e.g., such as the method of FIGs. 1-4) is shown in FIG. 6. The computer system 600 includes one or more computer hardware processors 610 and one or more articles of manufacture that comprise non-transitory computer-readable storage media (e.g., memory 620 and one or more non-volatile storage media 630). The processor 610 may control writing data to and reading data from the memory 620 and the non-volatile storage device 630 in any suitable manner, as the aspects of the technology described herein are not limited in this respect. To perform any of the functionality described herein, the processor(s) 610 may execute one or more processor-executable instructions stored in one or more non-transitory computer-readable storage media (e.g., the memory 620), which may serve as non-transitory computer-readable storage media storing processor-executable instructions for execution by the processor 610.

Computing device 600 may also include a network input/output (I/O) interface 640 via which the computing device may communicate with other computing devices (e.g., over a network), and may also include one or more user I/O interfaces 650, via which the computing device may provide output to and receive input from a user. The user I/O interfaces may include devices such as a keyboard, a mouse, a microphone, a display device (e.g., a monitor or touch screen), speakers, a camera, and/or various other types of I/O devices.

The above-described embodiments can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor (e.g., a microprocessor) or collection of processors, whether provided in a single computing device or distributed among multiple computing devices. It should be appreciated that any component or collection of components that perform the functions described above can be generically considered as one or more controllers that control the above-discussed functions. The one or more controllers can be implemented in numerous ways, such as with dedicated hardware, or with general purpose hardware (e.g., one or more processors) that is programmed using microcode or software to perform the functions recited above.

In this respect, it should be appreciated that one implementation of the embodiments described herein comprises at least one computer-readable storage medium (e.g., RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible, non-transitory computer-readable storage medium) encoded with a computer program (e.g. , a plurality of executable instructions) that, when executed on one or more processors, performs the above-discussed functions of one or more embodiments. The computer-readable medium may be transportable such that the program stored thereon can be loaded onto any computing device to implement aspects of the techniques discussed herein. In addition, it should be appreciated that the reference to a computer program which, when executed, performs any of the above-discussed functions, is not limited to an application program running on a host computer. Rather, the terms computer program and software are used herein in a generic sense to reference any type of computer code (e.g., application software, firmware, microcode, or any other form of computer instruction) that can be employed to program one or more processors to implement aspects of the techniques discussed herein.

The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of processor-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the disclosure provided herein need not reside on a single computer or processor, but may be distributed in a modular fashion among different computers or processors to implement various aspects of the disclosure provided herein. Processor-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.

Also, data structures may be stored in one or more non-transitory computer-readable storage media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a non-transitory computer-readable medium that convey relationship between the fields. However, any suitable mechanism may be used to establish relationships among information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationships among data elements.

Additional Embodiments

Additional embodiments of the present disclosure are encompassed by the following numbered paragraphs.

1. A method of evaluating the quality of a sample card comprising a biological sample and/or an image of the sample card, the sample card comprising a sample application zone, an optional sample flow zone, and a serum zone, the method comprising: using at least one computing device to perform: capturing an image of the sample card using a camera of the computing device or a scanner communicatively coupled to the computing device; determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique; determining at least one characteristic of the serum zone based on the identified location of the serum zone; and determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone.

2. The method of paragraph 1, wherein the at least one computing device further performs: providing instructions to a user based on the measure of quality of sample card and/or the image of the sample card.

3. The method of paragraph 1 or 2, further comprising preparing the sample card prior to obtaining the image by applying a liquid biological sample to the sample application zone and allowing the sample to flow across or within the sample card.

4. A method, comprising: applying a liquid biological sample from a subject to a sample application zone of a sample card; and using at least one computing device to perform: capturing an image of the sample card using a camera of the computing device or a scanner communicatively coupled to the computing device; determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique; determining at least one characteristic of the serum zone based on the identified location of the serum zone; determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone; and providing instructions to a user based on the measure of quality of the sample card and/or the image of the sample card.

5. The method of any one of paragraphs 2-4, wherein the instructions to the user comprise: (i) instructions to obtain a new biological sample on an unused sample card, (ii) instructions to deliver the sample card to a laboratory for analysis of the biological sample, or (iii) instructions to analyze the biological sample.

6. The method of paragraph 4 or 5, wherein a new biological sample is applied to a sample application zone of an unused sample card when the instructions to the user comprise: (i) instructions to obtain a new biological sample on an unused sample card, wherein the new biological sample is a second liquid biological sample from the same subject.

7. The method of paragraph 4 or 5, wherein the sample card is delivered to a laboratory for analysis of the biological sample when the instructions to the user comprise: (ii) instructions to deliver the sample card to a laboratory for analysis of the biological sample.

8. The method of paragraph 4 or 5, wherein the biological sample is analyzed when the instructions to the user comprise: (iii) instructions to analyze the biological sample.

9. The method of any preceding paragraph, wherein capturing an image of the sample card comprises capturing a photograph of the sample card or scanning the sample card.

10. The method of any preceding paragraph, wherein determining the location of the serum zone comprises: determining a location of the sample application zone using an edge detection technique; and determining the location of the serum zone based on the location of the sample application zone and information indicating a relative location of the serum zone to the sample application zone.

11. The method of paragraph 10, further comprising: determining at least one characteristic of the sample application zone based on the identified location of the sample application zone.

12. The method of any preceding paragraph, wherein determining the location of the serum zone comprises: determining a location of the sample flow zone using an edge detection technique; and determining the location of the serum zone based on the location of the sample flow zone and information indicating a relative location of the serum zone to the sample flow zone. 13. The method of paragraph 12, further comprising: determining at least one characteristic of the sample flow zone based on the identified location of the sample flow zone.

14. The method of any preceding paragraph, wherein the biological sample is whole blood, plasma, serum, urine, saliva, or any other body fluid.

15. The method of any preceding paragraph, wherein the biological sample is a sample from a subject, wherein the subject is a human, rodent, primate, dog, cat, bird, horse, cow, sheep, pig, veterinary animal, or any other mammal.

16. The method of any preceding paragraph, wherein the sample card is an ADx 100 sample card.

17. The method of any preceding paragraph, wherein the sample card is a rectangle or a square.

18. The method of paragraph 17, wherein the sample card has a length of 6-20 cm and a width of 2-10 cm.

19. The method of any preceding paragraph, wherein the computing device is a portable computing device.

20. The method of paragraph 19, wherein the portable computing device is a mobile computing device or tablet, smartphone, or laptop.

21. The method of any one of paragraphs 10-20, wherein the edge detection technique comprises Canny edge detection, a Hough transform, Sobel edge detection, Prewitt edge detection, and/or Laplacian edge detection.

22. The method of any one of paragraphs 10-21, wherein the edge detection technique further comprises erosion or dilation of the identified edges.

23. The method of any one of paragraphs 10-22, wherein the dimensions of the sample card are determined by identifying the edges of the largest rectangle or square.

24. The method of paragraph 23, wherein the dimensions of the sample card correspond to the dimensions used by a known sample card manufacturer.

25. The method of paragraph 23 or 24, wherein the dimensions of the sample card allow for identification of the brand, manufacturer and/or model of the sample card.

26. The method of any one of paragraphs 10-25, wherein the location of the sample application zone is determined by identifying the edges of the largest rectangle or square contained within a predetermined region-of-interest of the sample card.

27. The method of any preceding paragraph, wherein the location of the sample application zone is in a fixed location within the dimensions of the sample card. 28. The method of any preceding paragraph, wherein, following obtaining the image of the sample card, the image of the sample card is warped using an image analysis technique such that the dimensions of the sample card are flattened into a perfect rectangle or square.

29. The method of any preceding paragraph, wherein a sample flow zone is positioned between a sample application zone and the serum zone.

30. The method of any one of paragraphs 10-29, wherein, following determining a location of the sample application zone and/or the sample flow zone in the sample card, a serum region-of- interest comprising the serum zone is extracted from the image.

31. The method of paragraph 30, wherein the serum region-of-interest does not include any portion of the sample application zone or the sample flow zone.

32. The method of any preceding paragraph, wherein a location of the serum zone is determined by applying a segmentation technique to a serum region-of-interest.

33. The method of paragraph 32, wherein the segmentation technique is a watershed segmentation technique, a deformable model, or a texture analysis.

34. The method of paragraph 33, wherein the deformable model is an active shape or contour model, or a deformable model based on level sets.

35. The method of any one of paragraphs 32-34, wherein the segmentation technique utilizes seed points for the serum zone and background.

36. The method of paragraph 35, wherein the seed points for the serum zone are positioned from the edge of the sample flow zone or the serum region-of-interest by a distance that is equal to the height of the sample flow zone.

37. The method of paragraph 35, wherein the seed points for the serum zone are positioned in a fixed location relative to the location of the sample flow zone or the serum region-of-interest.

38. The method of any one of paragraphs 35-37, wherein the seed points for the background are positioned in a fixed location relative to the location of the sample flow zone or the serum region-of-interest.

39. The method of paragraph 35 , wherein the seed points for the serum zone constitute a line that is positioned at a fixed distance from the edge of the sample flow zone or serum region-of- interest.

40. The method of any one of paragraphs 35-39, wherein the fixed location of the seed points for the serum zone is 1-25 mm from an edge of the sample flow zone and/or is equidistant from two edges of the serum region-of-interest and/or is parallel to an edge of the serum region-of- interest.

41. The method of any one of paragraphs 35-40, wherein the fixed location of the seed points for the background is 0-10 mm from an edge of the serum region-of-interest and/or is equidistant from two edges of the serum region-of-interest and/or is perpendicular to an edge of the serum region-of-interest.

42. The method of any preceding paragraph, wherein the at least one characteristic of the zone(s) comprise color of the sample within the sample application zone, color of the sample within the sample flow zone, color of the serum zone, percentage of area within the sample application zone that contains sample, area of the sample flow zone, area of the serum zone, and/or number of discrete shapes within the serum zone.

43. The method of 42, wherein the color of the serum zone indicates the amount of residual red blood cells present within the serum zone.

44. The method of 42, wherein the discrete shapes within the serum zone are squares, rectangles, or circles, optionally wherein the squares are 10x10 mm squares.

45. A system, comprising: a portable computing device; and at least one non-transitory computer readable storage medium storing processor executable instructions that, when executed by the portable computing device, cause the portable computing device to perform: capturing an image of the sample card using a camera of the portable computing device or a scanner communicatively coupled to the portable device; determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique; determining at least one characteristic of the serum zone based on the identified location of the serum zone; determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone.

46. The system of paragraph 45, wherein the at least one computing device further performs: providing instructions to a user based on the measure of quality of the sample card and/or the image of the sample card.

47. At least one non-transitory computer readable storage medium storing processor executable instructions that, when executed by a portable computing device, cause the portable computing device to perform: capturing an image of the sample card using a camera of the portable computing device or a scanner communicatively coupled to the portable device; determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique; determining at least one characteristic of the serum zone based on the identified location of the serum zone; determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone.

48. The system of paragraph 47, wherein the at least one computing device further performs: providing instructions to a user based on the measure of quality of the sample card.

49. The system of paragraph 47, wherein the at least one computing device further performs: providing instructions to a user based on the measure of quality of the image of the sample card.

50. The system of paragraph 47, wherein the at least one computing device further performs: providing instructions to a user based on the measure of quality of the sample card and the image of the sample card.

51. A method of evaluating the quality of a sample card comprising a biological sample and/or an image of the sample card, the sample card comprising a sample application zone, an optional sample flow zone, and a serum zone, the method comprising: using at least one computing device to perform: capturing an image of the sample card using a camera of the computing device or a scanner communicatively coupled to the computing device; determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique; determining at least one characteristic of the serum zone based on the identified location of the serum zone; and determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone.

52. The method of paragraph 51, wherein the at least one computing device further performs: providing instructions to a user based on the measure of quality of sample card and/or the image of the sample card.

53. The method of paragraph 51 or 52, further comprising preparing the sample card prior to obtaining the image by applying a liquid biological sample to the sample application zone and allowing the sample to flow across or within the sample card.

54. A method, comprising: applying a liquid biological sample from a subject to a sample application zone of a sample card; and using at least one computing device to perform: capturing an image of the sample card using a camera of the computing device or a scanner communicatively coupled to the computing device; determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique; determining at least one characteristic of the serum zone based on the identified location of the serum zone; determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone; and providing instructions to a user based on the measure of quality of the sample card and/or the image of the sample card.

55. The method of any one of paragraphs 52-54, wherein the instructions to the user comprise: (i) instructions to obtain a new biological sample on an unused sample card, (ii) instructions to deliver the sample card to a laboratory for analysis of the biological sample, or (iii) instructions to analyze the biological sample.

56. The method of any one of paragraphs 51-55, wherein determining the location of the serum zone comprises: determining a location of the sample application zone using an edge detection technique; and determining the location of the serum zone based on the location of the sample application zone and information indicating a relative location of the serum zone to the sample application zone, optionally further comprising: determining at least one characteristic of the sample application zone based on the identified location of the sample application zone.

57. The method of any one of paragraphs 51-56, wherein determining the location of the serum zone comprises: determining a location of the sample flow zone using an edge detection technique; and determining the location of the serum zone based on the location of the sample flow zone and information indicating a relative location of the serum zone to the sample flow zone, optionally further comprising: determining at least one characteristic of the sample flow zone based on the identified location of the sample flow zone.

58. The method of any one of paragraphs 51-57, wherein the biological sample is whole blood, plasma, serum, urine, saliva, or any other body fluid, optionally wherein the biological sample is a sample from a subject, wherein the subject is a human, rodent, primate, dog, cat, bird, horse, cow, sheep, pig, veterinary animal, or any other mammal. 59. The method of any one of claims 56-58, wherein the edge detection technique comprises Canny edge detection, a Hough transform, Sobel edge detection, Prewitt edge detection, and/or Laplacian edge detection, optionally wherein the edge detection technique further comprises erosion or dilation of the identified edges.

60. The method of any one of paragraphs 51-59, wherein a location of the serum zone is determined by applying a segmentation technique to a serum region-of-interest, optionally wherein the segmentation technique is a watershed segmentation technique, a deformable model, or a texture analysis, further optionally wherein the segmentation technique utilizes seed points for the serum zone and background.

Claims

CLAIMS What is claimed is:

2. The method of claim 1, wherein the at least one computing device further performs: providing instructions to a user based on the measure of quality of sample card and/or the image of the sample card.

3. The method of claim 1 or 2, further comprising preparing the sample card prior to obtaining the image by applying a liquid biological sample to the sample application zone and allowing the sample to flow across or within the sample card.

4. A method, comprising: applying a liquid biological sample from a subject to a sample application zone of a sample card; and using at least one computing device to perform: capturing an image of the sample card using a camera of the computing device or a scanner communicatively coupled to the computing device; determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique;

39 determining at least one characteristic of the serum zone based on the identified location of the serum zone; determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone; and providing instructions to a user based on the measure of quality of the sample card and/or the image of the sample card.

5. The method of any one of claims 2-4, wherein the instructions to the user comprise: (i) instructions to obtain a new biological sample on an unused sample card, (ii) instructions to deliver the sample card to a laboratory for analysis of the biological sample, or (iii) instructions to analyze the biological sample.

6. The method of claim 4 or 5, wherein a new biological sample is applied to a sample application zone of an unused sample card when the instructions to the user comprise: (i) instructions to obtain a new biological sample on an unused sample card, wherein the new biological sample is a second liquid biological sample from the same subject.

7. The method of claim 4 or 5, wherein the sample card is delivered to a laboratory for analysis of the biological sample when the instructions to the user comprise: (ii) instructions to deliver the sample card to a laboratory for analysis of the biological sample.

8. The method of claim 4 or 5, wherein the biological sample is analyzed when the instructions to the user comprise: (iii) instructions to analyze the biological sample.

9. The method of any preceding claim, wherein capturing an image of the sample card comprises capturing a photograph of the sample card or scanning the sample card.

10. The method of any preceding claim, wherein determining the location of the serum zone comprises: determining a location of the sample application zone using an edge detection technique; and determining the location of the serum zone based on the location of the sample application zone and information indicating a relative location of the serum zone to the sample application zone.

40

11. The method of claim 10, further comprising: determining at least one characteristic of the sample application zone based on the identified location of the sample application zone.

12. The method of any preceding claim, wherein determining the location of the serum zone comprises: determining a location of the sample flow zone using an edge detection technique; and determining the location of the serum zone based on the location of the sample flow zone and information indicating a relative location of the serum zone to the sample flow zone.

13. The method of claim 12, further comprising: determining at least one characteristic of the sample flow zone based on the identified location of the sample flow zone.

14. The method of any preceding claim, wherein the biological sample is whole blood, plasma, serum, urine, saliva, or any other body fluid.

15. The method of any preceding claim, wherein the biological sample is a sample from a subject, wherein the subject is a human, rodent, primate, dog, cat, bird, horse, cow, sheep, pig, veterinary animal, or any other mammal.

16. The method of any preceding claim, wherein the sample card is an ADx 100 sample card.

17. The method of any preceding claim, wherein the sample card is a rectangle or a square.

18. The method of claim 17, wherein the sample card has a length of 6-20 cm and a width of

2-10 cm.

19. The method of any preceding claim, wherein the computing device is a portable computing device.

20. The method of claim 19, wherein the portable computing device is a mobile computing device or tablet, smartphone, or laptop.

21. The method of any one of claims 10-20, wherein the edge detection technique comprises Canny edge detection, a Hough transform, Sobel edge detection, Prewitt edge detection, and/or Laplacian edge detection.

41

22. The method of any one of claims 10-21, wherein the edge detection technique further comprises erosion or dilation of the identified edges.

23. The method of any one of claims 10-22, wherein the dimensions of the sample card are determined by identifying the edges of the largest rectangle or square.

24. The method of claim 23, wherein the dimensions of the sample card correspond to the dimensions used by a known sample card manufacturer.

25. The method of claim 23 or 24, wherein the dimensions of the sample card allow for identification of the brand, manufacturer and/or model of the sample card.

26. The method of any one of claims 10-25, wherein the location of the sample application zone is determined by identifying the edges of the largest rectangle or square contained within a predetermined region-of-interest of the sample card.

27. The method of any preceding claim, wherein the location of the sample application zone is in a fixed location within the dimensions of the sample card.

28. The method of any preceding claim, wherein, following obtaining the image of the sample card, the image of the sample card is warped using an image analysis technique such that the dimensions of the sample card are flattened into a perfect rectangle or square.

29. The method of any preceding claim, wherein a sample flow zone is positioned between a sample application zone and the serum zone.

30. The method of any one of claims 10-29, wherein, following determining a location of the sample application zone and/or the sample flow zone in the sample card, a serum region-of- interest comprising the serum zone is extracted from the image.

31. The method of claim 30, wherein the serum region-of-interest does not include any portion of the sample application zone or the sample flow zone.

32. The method of any preceding claim, wherein a location of the serum zone is determined by applying a segmentation technique to a serum region-of-interest.

33. The method of claim 32, wherein the segmentation technique is a watershed segmentation technique, a deformable model, or a texture analysis.

34. The method of claim 33, wherein the deformable model is an active shape or contour model, or a deformable model based on level sets.

35. The method of any one of claims 32-34, wherein the segmentation technique utilizes seed points for the serum zone and background.

36. The method of claim 35, wherein the seed points for the serum zone are positioned from the edge of the sample flow zone or the serum region-of-interest by a distance that is equal to the height of the sample flow zone.

37. The method of claim 35, wherein the seed points for the serum zone are positioned in a fixed location relative to the location of the sample flow zone or the serum region-of-interest.

38. The method of any one of claims 35-37, wherein the seed points for the background are positioned in a fixed location relative to the location of the sample flow zone or the serum region- of-interest.

39. The method of claim 35 , wherein the seed points for the serum zone constitute a line that is positioned at a fixed distance from the edge of the sample flow zone or serum region-of- interest.

40. The method of any one of claims 35-39, wherein the fixed location of the seed points for the serum zone is 1-25 mm from an edge of the sample flow zone and/or is equidistant from two edges of the serum region-of-interest and/or is parallel to an edge of the serum region-of-interest.

41. The method of any one of claims 35-40, wherein the fixed location of the seed points for the background is 0-10 mm from an edge of the serum region-of-interest and/or is equidistant from two edges of the serum region-of-interest and/or is perpendicular to an edge of the serum region-of-interest.

42. The method of any preceding claim, wherein the at least one characteristic of the zone(s) comprise color of the sample within the sample application zone, color of the sample within the sample flow zone, color of the serum zone, percentage of area within the sample application zone that contains sample, area of the sample flow zone, area of the serum zone, and/or number of discrete shapes within the serum zone.

46. The system of claim 45, wherein the at least one computing device further performs: providing instructions to a user based on the measure of quality of the sample card and/or the image of the sample card.

47. At least one non-transitory computer readable storage medium storing processor executable instructions that, when executed by a portable computing device, cause the portable computing device to perform:

44 capturing an image of the sample card using a camera of the portable computing device or a scanner communicatively coupled to the portable device; determining a location of a serum zone in the sample card at least in part by analyzing the image of the sample card using at least one image processing technique; determining at least one characteristic of the serum zone based on the identified location of the serum zone; determining a measure of quality of the sample card and/or the image of the sample card based on the at least one characteristic of the serum zone.

48. The system of claim 47, wherein the at least one computing device further performs: providing instructions to a user based on the measure of quality of the sample card and/or the image of the sample card.

45