WO2025147160A1 - Artificial intelligence-based device and method for inspecting and diagnosing frozen section of lymph node - Google Patents

Abstract

The present disclosure relates to an artificial intelligence-based device and method for inspecting and diagnosing a frozen section of a lymph node, and the present device may include: a memory; and a processor including an artificial intelligence engine for the inspection and diagnosis of the frozen section of the lymph node, wherein the processor acquires a first frozen section image of the lymph node, acquires a second frozen section image corresponding to the first frozen section image of the lymph node, and generates and provides a diagnosis result for the inspection of the frozen section of the lymph node on the basis of the first frozen section image and the second frozen section image by using the artificial intelligence engine.

Description

Artificial intelligence-based lymph node frozen section examination and diagnosis device and method

The present disclosure relates to an artificial intelligence-based lymph node frozen section examination and diagnosis device and method.

Frozen section examination is a method of pathological examination performed during surgery to determine the direction of the surgery or the extent of the surgery.

Typically, pathology specimens are made into pathology slides for pathological diagnosis through the process of fixation, dehydration, embedding, sectioning, and staining. It usually takes one day for the fixation, dehydration, and embedding steps to be completed.

On the other hand, frozen section examination has the advantage of being able to be performed during surgery, as it takes about 30 minutes to produce pathology slides after freezing, cutting, and staining without going through the fixation, dehydration, and embedding stages.

However, pathology slides produced in this way have many problems, such as the risk of misdiagnosis, as the cells are clumped and the staining pattern is irregular, which is disadvantageous in histomorphology compared to slides that have undergone conventional fixation, dehydration, and embedding.

Such misdiagnosis can have a decisive impact on, for example, deciding on the direction or extent of surgery, leading to irreversible results, and therefore requires countermeasures.

The present disclosure aims to provide a device and method for providing lymph node frozen section examination and diagnostic information based on artificial intelligence in conjunction with digital pathology.

An AI-based lymph node frozen section examination diagnostic device according to at least one of various embodiments of the present disclosure comprises: a memory; and a processor including an AI engine for the lymph node frozen section examination and diagnosis, wherein the processor can obtain a first frozen section image of the lymph node, obtain a second frozen section image corresponding to the first frozen section image of the lymph node, and generate and provide a diagnostic result for the frozen section examination of the lymph node based on the first frozen section image and the second frozen section image using the AI engine.

The processor according to at least one of the various embodiments of the present disclosure may generate a second frozen section image corresponding to the first frozen section image of the lymph node using the artificial intelligence engine.

The processor according to at least one of the various embodiments of the present disclosure can generate the second frozen section image by converting the first frozen section image into a HE (Histogram Equalization) image.

The artificial intelligence engine according to at least one of the various embodiments of the present disclosure may include a plurality of models required for preprocessing for learning.

The first model according to at least one of the various embodiments of the present disclosure may be a preprocessing model for learning diagnostic content from a frozen section image.

The first model according to at least one of the various embodiments of the present disclosure may be a model that learns by labeling frozen section images and diagnostic content.

The second model according to at least one of the various embodiments of the present disclosure may be a preprocessing model for learning related to conversion from a frozen section image to the HE image.

The second model according to at least one of the various embodiments of the present disclosure may be a model that learns by labeling frozen section images and HE images.

The third model according to at least one of the various embodiments of the present disclosure may be a preprocessing model for learning diagnostic content from a HE converted image.

The third model according to at least one of the various embodiments of the present disclosure may be a model that learns by labeling HE converted images and diagnostic content.

The processor according to at least one of the various embodiments of the present disclosure may determine at least two of the plurality of models as an ensemble.

The processor according to at least one of the various embodiments of the present disclosure can control changing the ensemble weights of each model.

The processor according to at least one of the various embodiments of the present disclosure can control changing of weights depending on the size of the tissue used in the frozen section image.

The processor according to at least one of the various embodiments of the present disclosure may set a high weight of a frozen section image if the size of the tissue is greater than or equal to a threshold, and may set a low weight of a HE converted image if the size of the tissue is less than the threshold.

An artificial intelligence-based lymph node frozen section examination diagnostic method, performed by a processor of an electronic device according to at least one of various embodiments of the present disclosure, may include the steps of: obtaining a first frozen section image of the lymph node; obtaining a second frozen section image corresponding to the first frozen section image of the lymph node; and generating and providing a diagnostic result for the frozen section examination of the lymph node based on the first frozen section image and the second frozen section image.

According to at least one of the various embodiments of the present disclosure, there is an advantage in that lymph node frozen section examination and diagnosis can be performed quickly and accurately through a device and method that provides lymph node frozen section examination and diagnosis information based on artificial intelligence in conjunction with digital pathology.

FIG. 1 is a schematic diagram of an artificial intelligence-based lymph node frozen section examination diagnostic system according to an embodiment of the present disclosure.

Figure 2 is a block diagram of the image acquisition device of Figure 1.

Figures 3 to 6 are block diagrams of the data processing unit or control unit of Figure 2.

FIG. 7 is a flowchart illustrating an artificial intelligence-based lymph node frozen section examination diagnostic method according to one embodiment of the present disclosure.

FIG. 8 is a diagram illustrating an example of a frozen section, a frozen section image, and a HE converted image of FIG. 7.

FIGS. 9 and 10 are flowcharts illustrating an artificial intelligence-based lymph node frozen section examination diagnostic method according to another embodiment of the present disclosure.

Throughout this disclosure, the same reference numerals refer to the same components. This disclosure does not describe all elements of the embodiments, and any content that is general in the technical field to which this disclosure belongs or that overlaps between the embodiments is omitted. The terms ‘part, module, element, block’ used in the specification can be implemented in software or hardware, and according to the embodiments, a plurality of ‘parts, modules, elements, blocks’ can be implemented as a single component, or a single ‘part, module, element, block’ can include a plurality of components.

Throughout the specification, when a part is said to be "connected" to another part, this includes not only a direct connection but also an indirect connection, and an indirect connection includes a connection via a wireless communications network.

Additionally, when a part is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise specifically stated.

Throughout the specification, when it is said that an element is "on" another element, this includes not only cases where the element is in contact with the other element, but also cases where there is another element between the two elements.

The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

Singular expressions include plural expressions unless the context clearly indicates otherwise.

The identification codes in each step are used for convenience of explanation and do not describe the order of each step. Each step may be performed in a different order than specified unless the context clearly indicates a specific order.

The operating principle and embodiments of the present disclosure are described below with reference to the attached drawings.

In this specification, the 'device according to the present disclosure' includes all of various devices that can perform computational processing and provide results to a user. For example, the device according to the present disclosure may include all of a computer, a server device, and a portable terminal, or may be in the form of any one of them.

Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, a web server, etc.

The above portable terminal may include, for example, all kinds of handheld-based wireless communication devices such as a PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminal, a smart phone, and a wearable device such as a watch, a ring, a bracelet, an anklet, a necklace, glasses, contact lenses, or a head-mounted device (HMD).

The function related to artificial intelligence according to the present disclosure is operated through a processor and a memory. The processor may be composed of one or more processors. At this time, one or more processors may be a general-purpose processor such as a CPU, an AP, a DSP (Digital Signal Processor), a graphics-only processor such as a GPU, a VPU (Vision Processing Unit), or an artificial intelligence-only processor such as an NPU. One or more processors control to process input data according to a predefined operation rule or artificial intelligence model stored in a memory. Alternatively, when one or more processors are artificial intelligence-only processors, the artificial intelligence-only processor may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that the basic artificial intelligence model is learned by using a plurality of learning data by a learning algorithm, thereby creating a predefined operation rules or artificial intelligence model set to perform a desired characteristic (or purpose). Such learning may be performed in the device itself on which the artificial intelligence according to the present disclosure is performed, or may be performed through a separate server and/or system. Examples of the learning algorithm include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

The artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values, and performs neural network operations through operations between the operation results of the previous layer and the multiple weights. The multiple weights of the multiple neural network layers may be optimized by the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value acquired from the artificial intelligence model is reduced or minimized during the learning process. The artificial neural network may include a deep neural network (DNN), and examples thereof include, but are not limited to, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or deep Q-networks.

According to an exemplary embodiment of the present disclosure, a processor can implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network that imitates human neurons (biological neurons) to enable a machine to learn. Artificial intelligence methodologies can be divided into supervised learning in which input data and output data are provided together as training data depending on the learning method, so that the solution (output data) to a problem (input data) is determined, unsupervised learning in which only input data is provided without output data, so that the solution (output data) to a problem (input data) is not determined, and reinforcement learning in which a reward is given from an external environment whenever an action is taken in a current state (State), and learning is performed in a direction to maximize this reward. In addition, artificial intelligence methodologies can be categorized by architecture, which is the structure of the learning model. The architectures of widely used deep learning technologies can be categorized into convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs).

The present device and system may include an artificial intelligence model. The artificial intelligence model may be one artificial intelligence model or may be implemented as multiple artificial intelligence models. The artificial intelligence model may be composed of a neural network (or an artificial neural network) and may include a statistical learning algorithm that mimics biological neurons in machine learning and cognitive science. A neural network may refer to a model in which artificial neurons (nodes) that form a network by combining synapses change the strength of the synapses through learning and have problem-solving capabilities. Neurons of a neural network may include a combination of weights or biases. A neural network may include one or more layers composed of one or more neurons or nodes. For example, the device may include an input layer, a hidden layer, and an output layer. A neural network that constitutes the device may infer a desired result (output) from an arbitrary input (input) by changing the weights of neurons through learning.

The processor can generate a neural network, train (or learn) a neural network, perform a calculation based on received input data, generate an information signal based on the result of the calculation, or retrain the neural network. The models of the neural network can include various types of models such as CNN (Convolution Neural Network) such as GoogleNet, AlexNet, VGG Network, R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), RNN (Recurrent Neural Network), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, etc., but are not limited thereto. The processor can include one or more processors for performing calculations according to the models of the neural network. For example, the neural network can include a deep It may include a deep neural network.

Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto) Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Depp Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), Generative Adversarial Network (GAN), Liquid State Machine (LSM), Extreme Learning Machine (ELM), It will be understood by those skilled in the art that any neural network may be included, including but not limited to an ESN (Echo State Network), a DRN (Deep Residual Network), a DNC (Differentiable Neural Computer), an NTM (Neural Turning Machine), a CN (Capsule Network), a KN (Kohonen Network), and an AN (Attention Network).

According to an exemplary embodiment of the present disclosure, the processor may be configured to perform a CNN (Convolution Neural Network) such as GoogleNet, AlexNet, VGG Network, R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), RNN (Recurrent Neural Network), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC/QA for natural language processing, Text Analysis, Dialog System, GPT-3, GPT-4, Visual Analytics for vision processing, Visual Understanding, Video Synthesis, ResNet for data intelligence, Anomaly Detection, Prediction, Time-Series Forecasting, Various artificial intelligence structures and algorithms such as Optimization, Recommendation, and Data Creation can be used, but are not limited thereto. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

As mentioned above, frozen section examination represents a method of pathological examination performed, for example, to determine the surgical direction or the surgical scope during surgery.

In this regard, the present specification discloses a digital pathology-linked artificial intelligence-based lymph node frozen section examination and diagnosis system, device and method according to at least one of the various embodiments of the present disclosure.

In the present disclosure, for example, for frozen section examination and diagnosis based on artificial intelligence, a lymph node frozen section examination reading algorithm, a virtual HE image conversion algorithm for a conventional fixation process (fixation, dehydration, embedding, thin section staining) of a lymph node frozen section image, a lymph node frozen section virtual HE conversion image reading algorithm, a lymph node frozen section examination reading and HE conversion image integration diagnosis algorithm, etc. are disclosed.

Therefore, according to the present disclosure, a user, for example, a medical institution, can be provided with HE converted images of lymph node frozen section images, lymph node frozen section image diagnosis, lymph node frozen section HE converted image diagnosis, final comprehensive diagnosis result information, etc.

The present disclosure can compare a first frozen section image and a second frozen section image. And the present disclosure can generate and provide AI-based diagnostic content information for each image. Then, a final diagnosis can be generated and provided based on the AI-based analysis results of the two images. In this way, according to the present disclosure, there is an advantage in that the accuracy of the diagnostic result can be increased and frozen section examination and diagnosis can be performed quickly and easily.

In the above, the first frozen section image is an original (or raw) frozen section image.

In the above, the second frozen section image may represent an image generated by converting the first frozen section image based on artificial intelligence. The second frozen section image may include, for example, a HE (Histogram Equalization) image. However, the present disclosure is not limited thereto.

Hereinafter, various embodiments of the present disclosure will be described with reference to the attached drawings.

FIG. 1 is a schematic diagram of an artificial intelligence-based lymph node frozen section examination diagnostic system (1) according to one embodiment of the present disclosure.

Figure 2 is a block diagram of the image acquisition device (10) of Figure 1.

Figures 3 to 6 are block diagrams of the data processing unit (220) or control unit (230) of Figure 2.

An artificial intelligence-based lymph node frozen section examination diagnostic system (1) according to at least one of the various embodiments of the present disclosure can provide a diagnostic result of a frozen section examination based on an image of a frozen section of a lymph node (hereinafter, “a first frozen section image”) and a second frozen section image converted to correspond to the first frozen section image. In this case, the second frozen section image can include an image converted using the HE method. However, the present disclosure is not limited thereto.

Referring to FIG. 1, an artificial intelligence-based lymph node frozen section examination diagnostic system (1) according to at least one of the various embodiments of the present disclosure may be configured to include an image acquisition device (10) and a terminal (30).

At this time, the artificial intelligence-based lymph node frozen section examination diagnostic system (1) according to at least one of the various embodiments of the present disclosure may further include a server (20). However, the server (20) may not be an essential component.

The server (20) can duplicate or replace all or part of the functions or components of the image acquisition device (10) described below.

These servers (20) may be provided in the form of a cloud and may be implemented to include various data processing modules for signal processing while being located remotely.

The image acquisition device (10) can obtain a frozen section image, i.e., a first frozen section image, as in (b) of FIG. 8, after the tissue, for example, as in (a) of FIG. 8, collected from a user (i.e., a target patient) for frozen section examination, is frozen and created as a frozen section slide (or pathology slide), through digital pathology.

The image acquisition device (10) can acquire a second frozen section image from the first frozen section image acquired after the digital pathology process for the target patient.

The image acquisition device (10) can generate frozen section examination results or diagnostic results for the biological tissue of a target patient based on the first frozen section image and the second frozen section image and provide the results to the terminal (30) via the terminal (30) or server (20).

All or part of the functions of the image acquisition device (10) described above may be handled by the server (20).

In this case, when a first frozen section image is uploaded from the image acquisition device (10), the server (20) can generate a second frozen section image based on the uploaded first frozen section image.

The server (20) can generate frozen section examination results or diagnostic results for the biological tissue of the target patient based on the first frozen section image and the second frozen section image and provide the results to the terminal (30) via the terminal (30) or/and the image acquisition device (10).

The terminal (30) requests a frozen section examination of the lymph node tissue of the target region to the image acquisition device (10) or/and the server (20), and as described above, obtains the results of the frozen section examination and provides related information through an output device (not shown) such as a display or speaker.

Meanwhile, depending on the embodiment, the terminal (30) may not be an essential component. For example, the terminal (30) may be replaced by the image acquisition device (10). In this case, it is preferable that the image acquisition device (10) be equipped with the aforementioned output device to provide relevant information.

In the above, the output device may include a display, a speaker (at this time, the speaker does not necessarily need to be built into the terminal (30), but a speaker that can be linked to the terminal (30) and output data is sufficient).

The connection between each component illustrated in Fig. 1 may refer to a wired/wireless communication method. For convenience of explanation, the present disclosure uses a wireless communication method such as Bluetooth, BLE (BluetoothTM Low Energy), Zigbee, or Wi-Fi as an example.

FIG. 2 illustrates components of an image acquisition device (10) according to at least one of various embodiments of the present disclosure.

At this time, the image acquisition device (10) can be viewed as, for example, an artificial intelligence-based lymph node frozen section examination diagnostic device.

An artificial intelligence-based lymph node frozen section examination diagnostic device (10) may be configured to include a memory (240) and a processor.

The processor may include an artificial intelligence engine (AI engine) for the above lymph node frozen section examination and diagnosis.

The processor may include a communication module (210), a data processing unit (220), a control unit (230), etc., to obtain a first frozen section image of a lymph node, obtain a second frozen section image corresponding to the first frozen section image of the lymph node, and generate and provide a diagnostic result for a frozen section examination of the lymph node based on the first frozen section image and the second frozen section image using the artificial intelligence engine.

The communication module (110) can provide a wireless communication interface environment to support a communication environment so that the artificial intelligence-based lymph node frozen section examination diagnostic device (10) can exchange data with the server (20) or/and the terminal (30). In this case, the communication module (110) can be an LTE, LTE-A, 5G, or Wi-Fi communication module.

The data processing unit (220) can process data acquired through the communication module (210) under the control of the control unit (240).

The control unit (230) can control the operation of each component of the artificial intelligence-based lymph node frozen section examination diagnostic device (10). The specific operation of the control unit (230) will be described later.

The memory (240) can store various information in advance or update stored information.

The memory (240) can temporarily store data obtained from an artificial intelligence-based lymph node frozen section examination diagnostic device (10) or data obtained from a terminal (30) or server (20).

The memory (240) may store information on software, such as applications, programs, etc., or related processing engines, etc., required for frozen section examination and diagnosis according to the present disclosure, as needed. The processing engine may include, for example, an artificial intelligence learning engine. Such an engine may be implemented in the data processing unit (220) or/and the control unit (230).

In FIG. 2, the memory (240) may include at least one type of storage medium among a flash memory type, a hard disk type, an SSD (Solid State Disk type), an SDD (Silicon Disk Drive type), a multimedia card micro type, a card type memory (for example, an SD or XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk.

In Fig. 2, only one memory (240) is illustrated for convenience, but it is not limited thereto and there may be multiple memory units.

In Fig. 2, the memory (240) is conveniently depicted and described as being built into the artificial intelligence-based lymph node frozen section examination diagnostic device (10), but is not limited thereto. For example, in Fig. 2, the memory (240) may also be implemented in the form of a DB (Database) server located externally or remotely.

In FIG. 2, the memory and the processor may be implemented as separate chips. Alternatively, the memory and the processor may be implemented as a single chip.

At least one component may be added or deleted in accordance with the performance of the components illustrated in FIG. 2. In addition, it will be readily understood by those skilled in the art that the mutual positions of the components may be changed in accordance with the performance or structure of the system.

Meanwhile, each component illustrated in FIG. 2 represents software and/or hardware components such as a Field Programmable Gate Array (FPGA) and an Application Specific Integrated Circuit (ASIC).

Below, the operation of a processor according to another embodiment of the present disclosure is described.

First, referring to FIG. 3, the data processing unit (220) or control unit (230) of the processor may be configured to include, for example, a data preprocessing unit (310), an artificial intelligence learning engine (320), an image comparison unit (330), a diagnostic information generation unit (340), etc.

Although some of the components illustrated in FIG. 3 may be included in the data processing unit (220) and the rest may be included in the control unit (230).

The data preprocessing unit (310) can preprocess input data to generate or extract data that can be used in the artificial intelligence learning engine (320).

The artificial intelligence learning engine (320) can learn a model using data (e.g., a learning dataset) preprocessed in the data preprocessing unit (310).

The artificial intelligence learning engine (320) may update the learned model based on data fed back from the server (20) and/or terminal (30). The model updated by the artificial intelligence learning engine (320) may be transmitted to the memory (240) and updated.

The image comparison unit (330) can compare and analyze input images.

The image comparison unit (330) can compare, for example, the first frozen section image and the second frozen section image that are input.

This image comparison unit (330) may be included as a component of, for example, an artificial intelligence learning engine (320) or a diagnostic information generation unit (340) described later.

The diagnostic information generation unit (340) can generate diagnostic information for a requested frozen section examination of the lymph node bio-tissue of the target patient based on the comparison results of the image comparison unit (330) using a model learned in the artificial intelligence learning engine (320). The diagnostic information generated in this way can be output to the terminal (30).

Next, referring to FIG. 4, the data processing unit (220) or/and the control unit (230) may be configured to include a data preprocessing unit (405) and k diagnostic models (where k is a natural number).

A diagnostic model may represent a learning model for generating diagnostic information.

The data preprocessing unit (405) can preprocess and classify data to be learned from each diagnostic model. The data classified in this way can be input into the corresponding diagnostic model and used for learning.

For example, assuming k is 3, the first diagnostic model can be a frozen section-based diagnostic learning model by labeling the frozen section image and the diagnostic content.

The second diagnostic model may be a diagnostic learning model that learns by labeling the lymph node frozen section image and the HE converted image to obtain the lymph node frozen section HE converted image from the lymph node frozen section image.

And the third diagnostic model may be a frozen section-based diagnostic learning model that labels the lymph node frozen section HE converted image and diagnostic content.

The processor can determine at least two diagnostic models as an ensemble depending on k. For example, the processor can determine the first diagnostic model and the third diagnostic model as an ensemble.

Even if the processor determines at least two diagnostic models as an ensemble, it may individually learn and utilize each diagnostic model determined as an ensemble.

Next, referring to FIG. 5, the data processing unit (220) or/and the control unit (230) may be configured to include a frozen section classification model (510) and m diagnostic models (where m is a natural number).

In the frozen section classification model (510), the diagnostic content used for learning may differ depending on the organ.

The frozen section classification model (510) can be classified to learn diagnostic information such as metastatic cancer, lymphoma, and benign in relation to lymph nodes, but is not limited thereto.

Therefore, the three (e.g., for lymph nodes, m is 3) diagnostic models may include a metastatic cancer diagnostic model (520), a lymphoma diagnostic model (530), and a benign diagnostic model (540).

Each diagnostic model can receive and learn preprocessed and classified data from the frozen section classification model (510).

Referring to FIG. 6, the data processing unit (220) and/or the control unit (230) may be configured to include a frozen section classification model (610) and r diagnostic models (where r is a natural number).

In the frozen section classification model (610), the diagnostic content used for learning may differ depending on the organ.

The frozen section classification model (610) can be classified so that each diagnostic model can learn according to the size of the lymph node biopsy tissue.

For convenience of explanation, assuming r to be 3, the diagnostic model may include a first size diagnostic model (620), a second size diagnostic model (630), and a third size diagnostic model (640).

Each diagnostic model can receive and learn preprocessed and classified data from the frozen section classification model (610).

The preprocessing and classification of the frozen section data in FIGS. 4 to 6 described above may be for the purpose of controlling the weights of the determined or to-be-determined ensemble, but is not limited thereto.

In connection with the present disclosure, the ensemble weights of each diagnostic model may be changed depending on the type of body organ. For example, if the body organ is a lymph node, a higher weight may be set to the third model in Fig. 4 compared to the other models.

In connection with the present disclosure, the weights may be controlled to be changed according to the size of the lymph node biopsy tissue. Although three sizes were given as an example in FIG. 6, when dividing into two sizes, if the size of the lymph node biopsy tissue for frozen section examination is greater than a threshold, a higher weight may be set to the lymph node, i.e., the first frozen section image, and otherwise, i.e., if the size of the lymph node biopsy tissue is less than the threshold, a lower weight may be set to the HE converted image, i.e., the second frozen section image.

As described above, the artificial intelligence engine according to at least one of the various embodiments of the present disclosure may include a plurality of models necessary for preprocessing for learning.

At this time, the first model may be a preprocessing model for learning about diagnostic content from frozen section images. The first model may be a model that learns by labeling frozen section images and diagnostic content.

And the second model may be a preprocessing model for learning related to converting a frozen section image into an HE image. The second model may be a model that learns by labeling the frozen section image and the HE image.

In addition, the third model may be a preprocessing model for learning about diagnostic content from HE converted images. The third model may be a model that learns by labeling HE converted images and diagnostic content.

The processor according to at least one of the various embodiments of the present disclosure may determine at least two of the plurality of models as an ensemble.

At this time, the processor can control changing the ensemble weights of each model.

FIGS. 7, 9 and 10 are flowcharts illustrating an artificial intelligence-based lymph node frozen section examination diagnostic method according to the present disclosure.

FIG. 8 is a diagram illustrating an example of a frozen section, a frozen section image, and a HE converted image of FIG. 7.

The operation method of an artificial intelligence-based lymph node frozen section examination diagnostic device (10) linked to digital pathology according to at least one of the various embodiments of the present disclosure may be as follows.

In operation S110, a lymph node biopsy tissue of a target patient can be collected for frozen section examination as shown in Fig. 8 (a).

In operation S120, lymph node biopsy specimens collected for frozen section examination in operation S110 may be frozen.

In operation S130, the frozen lymph node biopsy tissue of the target patient in operation S120 can be transferred to a pathology slide and digital pathology operation can be performed.

In operation S140, the processor can obtain a first frozen section image as illustrated in (b) of FIG. 8 based on the digital pathology results performed in operation S130.

In operation S150, the processor can HE-convert the first frozen section image acquired in operation S140 based on artificial intelligence to acquire a second frozen section image as illustrated in (c) of FIG. 8.

In operation S160, the processor can generate (or obtain) each image-based primary diagnostic information.

In operation S160, the processor can obtain primary diagnostic information generated based on the first frozen section image and primary diagnostic information generated based on the second frozen section image, respectively.

In operation S170, the processor can merge the primary diagnostic information acquired based on each frozen section image in operation S160 to generate secondary diagnostic information and provide it to a terminal (30), etc.

To summarize FIG. 7, a digital pathology-linked artificial intelligence-based lymph node frozen section examination diagnostic method according to at least one of various embodiments of the present disclosure acquires a first frozen section image of a lymph node, acquires a second frozen section image corresponding to the first frozen section image of the lymph node, and generates and provides a diagnostic result for the frozen section examination of the lymph node based on the first frozen section image and the second frozen section image.

Next, referring to FIG. 9, in operation S210, the processor can identify a body organ to be examined by frozen section.

In operation S220, the processor can determine ensemble weights of each diagnostic model based on the identified frozen section examination target body organ.

In operation S230, the processor can determine whether a change in the weights of the ensemble is required.

If the processor determines that the ensemble weights do not need to be changed based on the S230 operation judgment result, it can ignore or discard the previous decision.

In operation S240, if the processor determines that a change in the ensemble weights is necessary based on the judgment result of operation S230, it can control the change of the ensemble weights of each model.

Finally, referring to FIG. 10, in operation S310, the processor can identify the size of the target tissue for the cross-section inspection.

In operation S320, the processor can determine whether the size of the identified defect inspection target tissue is greater than or equal to a threshold.

If the processor determines that the size of the identified homogeneous section inspection target tissue is greater than the threshold as a result of the S320 operation judgment, a higher weight can be set to the homogeneous section (S330). At this time, the homogeneous section can represent the first homogeneous section image.

If the processor determines that the size of the identified homogeneous section inspection target tissue is less than the threshold as a result of the S320 operation judgment, it can assign a lower weight to the HE converted image (i.e., the second homogeneous section image) rather than the homogeneous section (S340).

When any event occurs, such as changing the weight of the ensemble among the aforementioned contents or changing the weight according to the size of the lymph node bio-tissue, the processor can notify the server (20) or/and the terminal (30) of the fact.

After the processor provides the diagnosis results of a lymph node biopsy of a target patient to the server (20) and/or the terminal (30), if positive feedback or negative feedback is received from the terminal (30), the processor can update the aforementioned diagnostic learning model by referring to the received feedback.

For example, if the negative feedback from the medical institution through the terminal (30) mentioned above is more than a predetermined number of times or is continuous, the processor can reset all or at least some of the learned diagnostic models and perform the learning process again. In this case, the preprocessing model can also be reset after initialization.

The processor can derive and store in advance the correlation of the result values according to the ensemble combination or ensemble weight in the memory (240). The correlation stored in this way can be reflected in a later update. For example, when one model is updated, other models with correlation can be updated together. In this case, the model updated according to the correlation can be updated to a lower degree than the target model.

The processor can store the target patient's gender, age group, situation and predicted information before frozen sectioning, situation and diagnosed information after frozen sectioning, etc. in the memory (240).

The processor can arbitrarily adjust the weights of the model and/or ensemble for a new target patient based on information stored in the memory (240).

The present disclosure can also provide information related to digital pathology-linked artificial intelligence-based lymph node frozen section examination and diagnosis in a combined form of two or more of the above-described embodiments.

Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium storing instructions executable by a computer. The instructions may be stored in the form of program codes, and when executed by a processor, may generate program modules to perform the operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

Computer-readable storage media include all types of storage media that store instructions that can be deciphered by a computer. Examples include ROM (Read Only Memory), RAM (Random Access Memory), magnetic tape, magnetic disk, flash memory, and optical data storage devices.

As described above, the disclosed embodiments have been described with reference to the attached drawings. Those skilled in the art to which the present disclosure pertains will understand that the present disclosure can be implemented in forms other than the disclosed embodiments without changing the technical idea or essential features of the present disclosure. The disclosed embodiments are exemplary and should not be construed as limiting.

Claims (15)

In an artificial intelligence-based lymph node frozen section examination diagnostic device, memory; and A processor including an artificial intelligence engine for the above lymph node frozen section examination and diagnosis, The above processor, Obtaining a first frozen section image of the lymph node, obtaining a second frozen section image corresponding to the first frozen section image of the lymph node, and generating and providing a diagnostic result for a frozen section examination of the lymph node based on the first frozen section image and the second frozen section image using the artificial intelligence engine. An artificial intelligence-based lymph node frozen section examination diagnostic device. In claim 1, The above processor, Using the artificial intelligence engine, a second frozen section image corresponding to the first frozen section image of the lymph node is generated. An artificial intelligence-based lymph node frozen section examination diagnostic device. In claim 2, The above processor, Converting the first frozen section image into a HE (Histogram Equalization) image to generate the second frozen section image. An artificial intelligence-based lymph node frozen section examination diagnostic device. In claim 3, The above artificial intelligence engine, Contains multiple models required for preprocessing for learning. An artificial intelligence-based lymph node frozen section examination diagnostic device. In claim 4, The above first model is a preprocessing model for learning diagnostic content from frozen section images. An artificial intelligence-based lymph node frozen section examination diagnostic device. In claim 5, The above first model is a model that learns by labeling frozen section images and diagnostic content. An artificial intelligence-based lymph node frozen section examination diagnostic device. In claim 6, The above second model is a preprocessing model for learning related to conversion from a frozen section image to the HE image. An artificial intelligence-based lymph node frozen section examination diagnostic device. In claim 7, The above second model is a model that learns by labeling frozen section images and HE images. An artificial intelligence-based lymph node frozen section examination diagnostic device. In claim 8, The above third model is a preprocessing model for learning diagnostic content from HE converted images. An artificial intelligence-based lymph node frozen section examination diagnostic device. In claim 9, The above third model is a model that learns by labeling HE converted images and diagnostic content. An artificial intelligence-based lymph node frozen section examination diagnostic device. In claim 10, The above processor, At least two of the above multiple models are determined as an ensemble, An artificial intelligence-based lymph node frozen section examination diagnostic device. In claim 11, The above processor, Controlling the ensemble weights of each of the above models, An artificial intelligence-based lymph node frozen section examination diagnostic device. In claim 12, The above processor, Controlling the weighting according to the size of the tissue used in the above frozen section image. An artificial intelligence-based lymph node frozen section examination diagnostic device. In claim 13, The above processor, If the size of the above organization is greater than the threshold, the weight of the frozen section image is set high, If the size of the above organization is less than a threshold, the weight of the HE converted image is set low. An artificial intelligence-based lymph node frozen section examination diagnostic device. In an artificial intelligence-based lymph node frozen section examination diagnostic method performed by a processor of an electronic device, A step of obtaining a first frozen section image of the above lymph node; A step of obtaining a second frozen section image corresponding to the first frozen section image of the lymph node; and A step of generating and providing a diagnostic result for a frozen section examination of a lymph node based on the first frozen section image and the second frozen section image, An artificial intelligence-based diagnostic method for lymph node frozen section examination.

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