WO2024202466A1 - Computer program, information processing method, and information processing device - Google Patents

Abstract

The present invention includes the steps of: acquiring tomographic image data obtained by scanning a blood vessel of a patient after an endovascular treatment and medical record information of the patient; calculating, on the basis of the acquired tomographic image data, blood vessel information indicating a characteristic feature of the blood vessel after the endovascular treatment; and inputting the calculated blood vessel information and the acquired medical record information into a trained model to calculate the risk of rehospitalization of the patient who has received the endovascular treatment, the trained model being configured to output the risk of rehospitalization of the patient who has received the endovascular treatment upon input of the blood vessel information based on the tomographic image data of the blood vessel and the medical record information of the patient.

Description

COMPUTER PROGRAM, INFORMATION PROCESSING METHOD, AND INFORMATION PROCESSING APPARATUS

The present invention relates to a computer program, an information processing method, and an information processing device.

Intravascular treatments, such as percutaneous coronary intervention (PCI), are used as minimally invasive treatments for ischemic heart diseases, such as angina pectoris or myocardial infarction. Intravascular imaging diagnostic devices, such as ultrasound imaging diagnostic devices using ultrasound (IVUS: IntraVascular Ultra Sound) and optical coherence imaging diagnostic devices using near-infrared rays (OCT: Optical Coherence Tomography), are used for preoperative diagnosis in endovascular treatments or to confirm the results after surgery (see, for example, Patent Document 1).

International Publication No. 2014/162368

The prognosis of patients who have undergone endovascular treatment is thought to be influenced by the experience and skills of the doctor performing the treatment, as well as the patient's medical history, but it is difficult to predict the risk of a patient being readmitted to hospital after endovascular treatment.

The objective of this disclosure is to provide a computer program, an information processing method, and an information processing device that can calculate the risk of readmission for patients who have undergone endovascular treatment.

The present invention provides a computer program that (1) causes a computer to execute a process of acquiring tomographic image data obtained by scanning a patient's blood vessels after endovascular treatment and the patient's medical record information, calculating vascular information indicating the characteristics of the blood vessels after endovascular treatment based on the acquired tomographic image data, and inputting the calculated vascular information and the acquired medical record information into a learning model that outputs the readmission risk of the patient who has undergone endovascular treatment when vascular information based on the tomographic image data of the blood vessels and the patient's medical record information are input.

(2) In the computer program of (1) above, it is preferable that the vascular information includes information regarding the presence or absence of vascular dissection, poor stent compression, poor stent expansion, protrusion, or minimum stent area (MSA), which are calculated based on tomographic image data after endovascular treatment.
(3) It is preferable that the computer program of (1) or (2) above acquires tomographic image data obtained by scanning the patient's blood vessels before endovascular treatment, calculates vascular information indicating characteristics of the blood vessels before endovascular treatment based on the acquired tomographic image data of the blood vessels before endovascular treatment, and calculates the risk of readmission of the patient who underwent endovascular treatment by inputting the calculated vascular information based on the tomographic image data of the blood vessels before endovascular treatment, the vascular information based on the tomographic image data of the blood vessels after endovascular treatment, and the acquired medical record information into the learning model.
(4) In the computer program described in any one of (1) to (3) above, it is preferable that the vascular information includes information relating to plaque burden, lipid plaque, attenuating plaque, calcified plaque, or unstable plaque calculated based on tomographic image data before endovascular treatment.
(5) In the computer program described in any one of (1) to (4) above, it is preferable that the tomographic image data includes ultrasound tomographic image data obtained by scanning a blood vessel using ultrasound, and optical coherence tomographic image data obtained by scanning a blood vessel using light.
(6) In the computer program according to any one of (1) to (5) above, it is preferable that the medical record information includes information relating to renal damage.

The information processing method of the present invention is (7) an information processing method for acquiring tomographic image data and the patient's medical record information obtained by scanning the patient's blood vessels after endovascular treatment, calculating vascular information indicating the characteristics of the blood vessels after endovascular treatment based on the acquired tomographic image data, and inputting the calculated vascular information and the acquired medical record information into a learning model that outputs the readmission risk of the patient who underwent endovascular treatment when vascular information based on the tomographic image data of the blood vessels and the patient's medical record information are input.

The information processing device of the present invention (8) is an information processing device that includes a learning model that outputs the risk of readmission of a patient who has undergone endovascular treatment when vascular information based on vascular tomographic image data and the patient's medical record information are input, an acquisition unit that acquires the tomographic image data and the patient's medical record information obtained by scanning the patient's blood vessels after endovascular treatment, and a processing unit that calculates vascular information indicating the characteristics of the blood vessels after endovascular treatment based on the acquired tomographic image data, and calculates the risk of readmission of a patient who has undergone endovascular treatment by inputting the calculated vascular information and the acquired medical record information.

According to this disclosure, it is possible to calculate the risk of readmission for patients who have undergone endovascular treatment.

1 is a block diagram showing an example of the configuration of an information processing device according to a first embodiment; FIG. 2 is an explanatory diagram showing an information processing method according to the first embodiment; 4 is a flowchart showing an information processing procedure according to the first embodiment. FIG. 13 is a schematic diagram showing an example of a readmission risk display screen. FIG. 11 is an explanatory diagram showing an information processing method according to a second embodiment.

A computer program, an information processing method, and an information processing device according to embodiments of the present disclosure will be described below with reference to the drawings. Note that the present disclosure is not limited to these examples, but is indicated by the claims, and is intended to include all modifications within the meaning and scope equivalent to the claims. In addition, at least some of the embodiments described below may be combined in any desired manner.

(Embodiment 1)
1 is a block diagram showing an example of the configuration of an information processing device 1 according to embodiment 1. The information processing device 1 is a computer, and includes a processing unit 11, a storage unit 12, a display unit 13, an operation unit 14, and a communication unit 15. Each unit is connected via a bus. Note that the information processing device 1 may be a multi-computer consisting of multiple computers, or may be a virtual machine virtually constructed by software.

The processing unit 11 is an arithmetic processing device or processor having one or more CPUs (Central Processing Units), MPUs (Micro-Processing Units), GPUs (Graphics Processing Units), GPGPUs (General-purpose computing on graphics processing units), TPUs (Tensor Processing Units), etc. The processing unit 11 reads out and executes a computer program P (program product) stored in the memory unit 12, thereby implementing the information processing method of embodiment 1 and calculating the risk of readmission of a patient who has undergone endovascular treatment.

The communication unit 15 includes a communication circuit for performing communication processing with the diagnostic imaging device 3. The communication unit 15 transmits and receives various information with the diagnostic imaging device 3.

The imaging diagnostic device 3 constitutes an imaging diagnostic system using, for example, a dual-type catheter equipped with both intravascular ultrasound (IVUS) and optical coherence tomography (OCT) functions. The imaging diagnostic device 3 includes an imaging diagnostic catheter, an MDU (Motor Drive Unit), and an image processing device. The imaging diagnostic catheter has a sensor unit that emits ultrasound or light at the tip. The MDU can perform a pull-back operation to rotate the sensor unit and shaft inserted into the probe of the imaging diagnostic catheter in a circumferential direction while pulling them toward the MDU side at a constant speed. The sensor unit rotates while moving from the tip side to the base end by the pull-back operation, and continuously scans the inside of the blood vessel at a predetermined time interval, and the imaging diagnostic device 3 can continuously generate multiple tomographic images approximately perpendicular to the probe based on the scanning results. The image processing unit acquires signal data based on reflected waves of ultrasound or light emitted from the sensor unit in the radial direction of the diagnostic imaging catheter toward the tubular organ, and generates IVUS image data (ultrasound tomographic image data) and OCT image data (optical coherence tomographic image data) that visualize the reflection intensity versus the distance from the sensor unit based on the acquired signal data. The diagnostic imaging device 3 transmits the generated IVUS image data and OCT image data to the information processing device 1. The information processing device 1 receives the IVUS image data and OCT image data transmitted from the diagnostic imaging device 3 at the communication unit 15. Hereinafter, the IVUS image data and OCT image data will be collectively referred to as tomographic image data.

The memory unit 12 has, for example, a main memory unit and an auxiliary memory unit. The main memory unit is a temporary storage area such as SRAM (Static Random Access Memory), DRAM (Dynamic Random Access Memory), or flash memory, and temporarily stores data necessary for the processing unit 11 to execute arithmetic processing. The auxiliary memory unit is a storage device such as a hard disk or EEPROM (Electrically Erasable Programmable ROM). The auxiliary memory unit stores the computer program P executed by the processing unit 11, the learning model 2, the electronic medical record DB 12a, the tomographic image DB 12b, and various other data necessary for processing. Details of the learning model 2 will be described later.

The electronic medical record DB 12a stores the medical record information of patients undergoing endovascular treatment. For example, the patient's name, date of birth, sex, height, weight, and medical history are stored in a patient ID that identifies the patient. It is preferable that the medical record information includes information indicating the presence or absence of renal impairment.

The tomographic image DB 12b stores the IVUS image data and OCT image data of the patient that the information processing device 1 receives via the communication unit 15. Specifically, the tomographic image DB 12b stores the IVUS image data and OCT image data before the endovascular treatment and the IVUS image data and OCT image data after the endovascular treatment in association with the patient ID.

The auxiliary storage unit may be an external storage device connected to the information processing device 1. The computer program P may be written to the auxiliary storage unit during the manufacturing stage of the information processing device 1, or the computer program P may be distributed by an external information processing device 1 and acquired by the information processing device 1 via communication and stored in the auxiliary storage unit. The computer program P may be recorded in a readable manner on a recording medium 1a such as a magnetic disk, optical disk, or semiconductor memory, and may be read from the recording medium 1a by a reading device and stored in the auxiliary storage unit.

The display unit 13 is, for example, a display device such as a liquid crystal panel or an organic EL (Electro Luminescence) display.

The operation unit 14 is, for example, an input device such as a touch panel, a hardware keyboard, or a mouse built into the display unit 13. A user (medical personnel) can operate the information processing device 1 using the operation unit 14 to input information required for the readmission risk of patients who have undergone endovascular treatment and to display the readmission risk.

FIG. 2 is an explanatory diagram showing an information processing method according to the first embodiment. The information processing device 1 includes a first feature calculation unit 11a and a second feature calculation unit 11b as functional units of the processing unit 11.

The first feature calculation unit 11a calculates vascular information representing the characteristics of vascular lesions from the IVUS image data and OCT image data of the patient before endovascular treatment. The vascular information calculated from the IVUS image data and OCT image data before endovascular treatment includes information related to plaque burden, lipid plaque, attenuating plaque, calcified plaque, and unstable plaque. The vascular information calculated in this manner is input to the learning model 2.

The information related to plaque burden includes the ratio of the plaque cross-sectional area to the blood vessel cross-sectional area. It is preferable that the plaque burden is calculated using both IVUS image data and OCT image data. The first feature calculation unit 11a calculates the blood vessel diameter based on the IVUS image data and calculates the lumen diameter based on the OCT image data. The blood vessel diameter is a distance obtained by detecting a closed curve showing the boundary of the external elastic lamina (blood vessel) on the image, setting the center of gravity based on the detected closed curve, and then measuring the linear distance between the center of gravity and the closed curve on the image. Since the linear distance between the center of gravity and the closed curve differs depending on the direction from the center of gravity to the closed curve, the blood vessel diameter may be a statistical value such as the average diameter, maximum diameter, and minimum diameter of the distance measured at multiple circumferential positions. The method of calculating the blood vessel diameter is not particularly limited, and it is sufficient that the square value roughly corresponds to the cross-sectional area of the inner space of the blood vessel when a state in which no plaque is attached is assumed. Similarly, the lumen diameter is a distance obtained by detecting a closed curve showing the boundary of the lumen on an image, setting a center of gravity based on the detected closed curve, and then measuring the linear distance between the center of gravity and the closed curve on the image. Since the linear distance between the center of gravity and the closed curve differs depending on the direction from the center of gravity to the closed curve, the lumen diameter may be a statistical value such as an average diameter, a maximum diameter, or a minimum diameter of the distance measured at a plurality of circumferential positions. The method of calculating the lumen diameter is not particularly limited, and the square value may be any value that roughly corresponds to the cross-sectional area of the inner space of the blood vessel to which plaque is attached. In short, the blood vessel diameter may be a numerical value that represents the size of the inner space of the blood vessel before plaque is attached, and the lumen diameter may be a numerical value that represents the size of the inner space of the blood vessel to which plaque is attached. The first feature amount calculation unit 11a calculates the ratio (plaque burden) of the cross-sectional area of the plaque to the blood vessel media area (blood vessel cross-sectional area) based on the calculated blood vessel diameter and lumen diameter. The plaque burden is expressed as (blood vessel media area - blood vessel lumen area) / blood vessel media area. Then, the first feature amount calculator 11a calculates the maximum or average value of plaque burden (plaque burden per unit length) as vascular information. The average plaque burden is expressed as the sum of the plaque cross-sectional area over the entire lesion/lesion length.

The information related to lipid plaque includes the presence or absence of lipid plaque, the number of lipid plaques, the maximum angle indicating the circumferential spread of lipid plaque in the cross section of the blood vessel, the area of lipid plaque, etc. The area of lipid plaque includes, for example, the maximum or average value of the lipid plaque area in the lesion (lipid plaque area per unit length). The average value is expressed as the sum of the lipid plaque area over the entire lesion/lesion length. It is preferable that the information related to lipid plaque is calculated based on OCT image data and IVUS image data.

Information related to attenuating plaque includes the presence or absence of attenuating plaque, the number of attenuating plaques, the maximum angle indicating the circumferential spread of attenuating plaque in the cross section of the blood vessel, the area of attenuating plaque, etc. The area of attenuating plaque includes, for example, the maximum or average value of the attenuating plaque area in the lesion (attenuating plaque area per unit length). The average value is expressed as the sum of the attenuating plaque areas over the entire lesion/lesion length. Attenuating plaque is, for example, lipid plaque. Attenuating plaque is plaque defined based on IVUS image data, and is therefore calculated based on IVUS image data.

Information related to calcified plaque includes the presence or absence of calcified plaque, the number of calcified plaques, the maximum angle indicating the circumferential spread of calcified plaque in the cross section of the blood vessel, the area of calcified plaque, etc. The area of calcified plaque includes, for example, the maximum or average value of the area of calcified plaque in the lesion (calcified plaque area per unit length). The average value is expressed as the total value of the area of calcified plaque over the entire lesion/lesion length. It is preferable that the presence or absence and the number of calcified plaques are calculated based on IVUS image data. It is preferable that the area of calcified plaques is calculated based on OCT image data.

Information related to unstable plaque includes the presence or absence of unstable plaque, the number of unstable plaques, the area of unstable plaques, etc. It is preferable that information related to unstable plaques is calculated using both IVUS image data and OCT image data.

The second feature calculation unit 11b calculates vascular information representing the characteristics of the vascular lesion from the IVUS image data and OCT image data of the patient after endovascular treatment. The vascular information calculated from the IVUS image data and OCT image data after endovascular treatment includes information related to the presence or absence of vascular dissection, poor stent crimping, poor stent expansion, tissue protrusion (vascular tissue protruding into the lumen from the stent mesh), and minimum stent area. The vascular information calculated in this way is input to learning model 2.

Information related to vascular dissection includes, for example, the presence or absence of a vascular dissection, the maximum angle indicating the circumferential extent of the vascular dissection in the vascular cross section, and the length of the vascular dissection in the longitudinal direction of the vascular dissection.

Information related to stent poor crimping includes, for example, the presence or absence of stent poor crimping, the maximum distance between the stent and the blood vessel wall in the area where the stent poor crimping occurs, and the length of the blood vessel in the longitudinal direction in the area where the stent poor crimping occurs.

Information related to poor stent expansion includes the ratio of the blood vessel diameter in the normal area to the minimum stent area. It is preferable that the minimum stent area is calculated using OCT image data, and the blood vessel diameter in the normal area is calculated using IVUS image data.

The learning model 2 is a trained model that has been trained to output a patient's risk of readmission from vascular information based on IVUS and OCT images and the patient's medical record information through machine learning using teacher data. The learning model 2 performs a predetermined calculation on the input values and outputs the calculation results, and the memory unit 12 stores data such as coefficients and thresholds of functions that define this calculation as the learning model 2. By reading the data stored as the learning model 2, the processing unit 11 becomes able to execute calculation processing of the risk of readmission from vascular information and medical record information extracted from IVUS and OCT images.

In this embodiment 1, the learning process of the learning model 2 is performed by a learning computer. Data related to the learned learning model 2 may be provided in the form of distribution via a communication network, similar to the computer program P, or may be provided in the form of being recorded on a recording medium 1a.

In this embodiment 1, the learning model 2 is a neural network having an input layer 2a to which vascular information calculated based on IVUS images and OCT images of the patient before endovascular treatment, vascular information calculated based on IVUS images and OCT images of the patient after endovascular treatment, and medical record information of the patient are input, an intermediate layer 2b that extracts features of the vascular information and medical record information, and an output layer 2c that outputs the risk of readmission calculated based on the extracted features.

The input layer 2a of the neural network has multiple neurons to which vascular information and medical record information are input, and passes each input data to the intermediate layer 2b.

The intermediate layer 2b has multiple layers made up of multiple neurons. Each layer extracts features related to the risk of readmission from the input data and passes them on to the next layer in turn, with the last layer passing them on to the output layer 2c.

The output layer 2c includes neurons that output the calculation results, and these neurons output the readmission risk of a patient who has undergone endovascular treatment. The readmission risk is, for example, a real value between 0 and 1. "0" indicates the lowest readmission risk, and "1" indicates the highest readmission risk.

In this embodiment 1, an example has been described in which the learning model 2 is a general neural network, but it may also be a model having a configuration such as other neural networks such as a transformer, an SVM (Support Vector Machine), a Bayesian network, or a regression tree.

A learning method for the learning model 2 will be described. First, the computer collects a plurality of IVUS images, OCT images, and medical record information that are the source of the training data. Then, the computer generates training data by adding training data indicating the risk of re-admission of the patient to the vascular information and medical record information calculated based on the IVUS images and OCT images of the patient. Next, the computer generates the learning model 2 by performing machine learning or deep learning on the pre-training neural network model using the generated training data.
Specifically, the computer inputs the vascular information and medical record information contained in the learning data into the pre-learning neural network model, and obtains the readmission risk output from the output layer 2c through arithmetic processing in the intermediate layer 2b. The computer then compares the readmission risk output from the output layer 2c with the readmission risk indicated by the teacher data, and optimizes the parameters used in the arithmetic processing in the intermediate layer 2b so that the readmission risk output from the output layer 2c approaches the correct value. The parameters are, for example, weights (coupling coefficients) between neurons. There are no particular limitations on the method of optimizing the parameters, but for example, the computer optimizes various parameters using the steepest descent method or the like.

The information processing device 1 obtains a trained learning model 2 by repeatedly performing the above process based on the training data of a large number of patients contained in the training data.

FIG. 3 is a flowchart showing the information processing procedure according to the first embodiment. The processing unit 11 of the information processing device 1 reads out the medical record information of a patient who is to be evaluated for risk of readmission from the electronic medical record DB 12a (step S11).

Next, the processing unit 11 acquires IVUS image data and OCT image data before and after endovascular treatment of the patient who is to be evaluated for risk of readmission (step S12). The processing unit 11 may be configured to acquire the IVUS image data and OCT image data from the imaging diagnostic device 3 via the communication unit 15, or may be configured to read out these data from the tomographic image DB 12b.

Then, the processing unit 11, which functions as the first feature calculation unit 11a, calculates vascular information such as information related to plaque burden, lipid plaque, attenuating plaque, calcified plaque, and unstable plaque based on the IVUS image data and OCT image data before the endovascular treatment (step S13).

Then, the processing unit 11, which functions as the second feature calculation unit 11b, calculates vascular information such as information related to vascular dissection, poor stent compression, and poor stent expansion based on the IVUS image data and OCT image data after endovascular treatment (step S14).

Then, the processing unit 11 inputs the vascular information before the endovascular treatment calculated in step S13, the vascular information calculated in step S14, and the medical record information acquired in step S11 into the learning model 2 to calculate the patient's risk of readmission (step S15). The processing unit 11 then displays the patient's risk of readmission on the display unit 13 (step S16), and ends the process.

FIG. 4 is a schematic diagram showing an example of the readmission risk display screen 4. The processing unit 11 may be configured to display the readmission risk display screen 4 as shown in FIG. 4 in step S16. The readmission risk display screen 4 includes, for example, a medical record information display section 41, a pre-treatment tomographic image display section 42, a post-treatment tomographic image display section 43, a pre-treatment vascular information display section 44, a post-treatment vascular information display section 45, a readmission risk display gauge 46, and an output result explanation section 47.

The medical record information display unit 41 is a frame that displays the electronic medical record information of the patient who is the subject of an evaluation of the risk of readmission. The processing unit 11 displays the medical record information acquired in step S11 on the medical record information display unit 41.

The pre-treatment tomographic image display section 42 and the post-treatment tomographic image display section 43 are frames that display tomographic image data before and after endovascular treatment.

The pre-treatment vascular information display section 44 is a frame that displays vascular information before endovascular treatment, and the post-treatment vascular information display section 45 is a frame that displays vascular information after endovascular treatment.

The readmission risk display gauge 46 includes a bar to indicate the degree of risk, ranging from low risk to high risk, and a line image indicating the degree of risk, and indicates the patient's risk of readmission depending on the display position of the line image.

The output result explanation unit 47 displays the importance of each of the multiple vascular information and medical record information with respect to the readmission risk output by the learning model 2. The processing unit 11 can calculate the importance of each of the multiple vascular information and medical record information with respect to the readmission risk output by the learning model 2, for example, by using XAI (XAI: Explainable AI) technology. XAI is a technology that outputs the factor contribution rate for each piece of information input to the machine learning model and presents to humans in an explainable manner which input information led to the output result (readmission risk). XRAI (EXplanation with Ranked Area Integrals) can be used as the XAI technology.
The output result explanation unit 47 displays, for example, the importance of each of the various vascular information before and after endovascular treatment, and the electronic medical record information, i.e., the magnitude of the influence on the output result of the learning model 2, in numerical or graphical form. In the example shown in Fig. 4, the output result explanation unit 47 displays characters representing the various vascular information before and after endovascular treatment, and the electronic medical record information, in association with a meter bar indicating the importance of each piece of information. The magnitude of the importance of each piece of information is represented by the length of the meter bar.

The information processing device 1 configured in this manner can calculate the risk of readmission of a patient who has undergone endovascular treatment based on the tomographic image data of the patient's blood vessels and the patient's medical record information.

In particular, in this embodiment 1, by using both IVUS image data obtained by scanning blood vessels with ultrasound and OCT data obtained by scanning blood vessels with light, more detailed vascular information of the patient can be obtained, and the patient's risk of re-admission can be calculated more accurately.

In addition, in this embodiment 1, by using IVUS image data and OCT image data before and after endovascular treatment, the patient's risk of re-admission can be calculated more accurately.

(Embodiment 2)
The information processing device 1 according to the second embodiment differs from the first embodiment in that IVUS image data and OCT image data are also input to the learning model. Since the other configurations of the information processing device 1 are similar to those of the information processing device 1 according to the first embodiment, the same reference numerals are used for similar parts and detailed description will be omitted.

5 is an explanatory diagram showing an information processing method according to the second embodiment. The learning model 202 includes, for example, a convolutional neural network (CNN) that has been trained by deep learning. The learning model 202 according to the second embodiment is a neural network having an input layer 202a to which, for example, vascular information calculated based on IVUS images and OCT images of a patient before endovascular treatment, vascular information calculated based on IVUS images and OCT images of a patient after endovascular treatment, IVUS image data before and after endovascular treatment, OCT image data before and after endovascular treatment, and medical record information of the patient are input, an intermediate layer 202b that extracts features of the vascular information, tomographic image data, and medical record information, and an output layer 202c that outputs a readmission risk calculated based on the extracted features.

In step S15 described in embodiment 1, the processing unit 11 inputs vascular information before and after endovascular treatment, IVUS image data before and after endovascular treatment, OCT image data before and after endovascular treatment, and medical record information into the learning model 202 to calculate the patient's risk of readmission.

According to the second embodiment, vascular information that cannot be extracted by rule-based processing can be extracted from tomographic image data, and the risk of readmission to the patient can be calculated more accurately.

1: Information processing device 1a: Recording medium 2: Learning model 3: Image diagnostic device 4: Readmission risk display screen 11: Processing unit 12: Memory unit 13: Display unit 14: Operation unit 15: Communication unit 41: Medical record information display unit 42: Pre-treatment tomographic image display unit 43: Post-treatment tomographic image display unit 44: Pre-treatment vascular information display unit 45: Post-treatment vascular information display unit 46: Readmission risk display gauge 47: Output result explanation unit 202: Learning model P: Computer program

Claims (8)

Obtaining cross-sectional image data obtained by scanning the patient's blood vessel after the endovascular treatment and the patient's medical record information;
Calculating vascular information that indicates characteristics of blood vessels after endovascular treatment based on the acquired tomographic image data;
A computer program that causes a computer to execute a process of calculating the risk of readmission of a patient who has undergone endovascular treatment by inputting the calculated vascular information and the acquired medical record information into a learning model that outputs the risk of readmission of the patient who has undergone endovascular treatment when vascular information based on vascular tomographic image data and the patient's medical record information are input. The blood vessel information is
The computer program according to claim 1 , further comprising information regarding the presence or absence of vascular dissection, stent crimping failure, stent expansion failure, protrusion, or minimum stent area, which is calculated based on the tomographic image data after endovascular treatment. acquiring cross-sectional image data obtained by scanning a patient's blood vessel prior to endovascular treatment;
Calculating vascular information indicating characteristics of the blood vessel before the endovascular treatment based on the acquired tomographic image data of the blood vessel before the endovascular treatment;
3. The computer program according to claim 1 or 2, which calculates the risk of readmission of a patient who has undergone endovascular treatment by inputting vascular information based on the calculated tomographic image data of the blood vessel before endovascular treatment, vascular information based on the calculated tomographic image data of the blood vessel after endovascular treatment, and acquired medical record information into the learning model. The blood vessel information is
The computer program according to claim 3 , further comprising information relating to plaque burden, lipid plaque, attenuating plaque, calcified plaque, or unstable plaque, which is calculated based on tomographic image data before endovascular treatment. The computer program product according to claim 4 , wherein the tomographic image data includes ultrasonic tomographic image data obtained by scanning a blood vessel with ultrasonic waves, and optical coherence tomographic image data obtained by scanning a blood vessel with light. The computer program according to claim 1 or 2, wherein the medical record information includes information relating to renal disorders. Obtaining cross-sectional image data obtained by scanning the patient's blood vessel after the endovascular treatment and the patient's medical record information;
Calculating vascular information that indicates characteristics of blood vessels after endovascular treatment based on the acquired tomographic image data;
An information processing method for calculating the risk of readmission of a patient who has undergone endovascular treatment by inputting the calculated vascular information and the acquired medical record information into a learning model that outputs the risk of readmission of the patient who has undergone endovascular treatment when vascular information based on tomographic image data of the blood vessels and the patient's medical record information are input. a learning model that outputs a risk of readmission of a patient who has undergone endovascular treatment when vascular information based on vascular tomographic image data and patient chart information are input;
an acquisition unit that acquires tomographic image data obtained by scanning a patient's blood vessel after an intravascular treatment and medical record information of the patient;
and a processing unit that calculates vascular information indicating characteristics of blood vessels after endovascular treatment based on the acquired tomographic image data, and calculates the risk of readmission of a patient who has undergone endovascular treatment by inputting the calculated vascular information and the acquired medical record information.

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