Computer program, information processing method, and information processing device

Abstract

The present invention generates a series of multiple images on the basis of signals received from a scanning probe that scans an organ of a subject, determines whether the plurality of images generated are suitable for diagnosing a prescribed disease, and causes a computer to execute a process for outputting the collected number of images which are suitable for diagnosing the prescribed disease.

Description

[Technical Field]

[0001] The present invention relates to a computer program, an information processing method, and an information processing device. [Background technology]

[0002] Patent Document 1 discloses an ultrasound guidance device that creates a guidance plan regarding a method for guiding an operator of an ultrasound diagnostic device, and guides the operator so that an echo image of a subject containing a specific anatomical image is captured. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2019-521745 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the ultrasound guidance device of Patent Document 1 only guides the operation of the ultrasound diagnostic device based on a guidance plan, and whether images suitable for diagnosing a predetermined disease can be obtained even if the operation guide is followed depends on the skill of the surgeon. Furthermore, if a certain number of images suitable for diagnosing a predetermined disease cannot be selected from the obtained images, there is a problem that a highly accurate diagnosis cannot be made.

[0005] An object of the present invention is to provide a computer program, an information processing method, and an information processing device that can select and collect images suitable for diagnosing a predetermined disease from a series of multiple images obtained by a scanning probe that scans the organs of a subject, and output the amount of collected images. [Means for solving the problem]

[0006] The computer program according to this aspect causes a computer to perform the following processes: generate a series of images based on signals obtained from a scanning probe that scans an organ of a subject; determine whether the generated images are suitable for diagnosing a predetermined disease; store the images suitable for diagnosing the predetermined disease; and output a collection of the images suitable for diagnosing the predetermined disease.

[0007] The information processing method according to this aspect generates a series of images based on signals obtained from a scanning probe that scans an organ of a subject, determines whether the generated images are suitable for diagnosing a predetermined disease, stores the images suitable for diagnosing the predetermined disease, and outputs a collection of the images suitable for diagnosing the predetermined disease.

[0008] The information processing device of this aspect includes a generation unit that generates a series of multiple images based on signals obtained from a scanning probe that scans an organ of a subject, a determination unit that determines whether the multiple images generated by the generation unit are suitable for diagnosing a specified disease, a memory unit that stores the images that are determined by the determination unit to be suitable for diagnosing the specified disease, and an output unit that outputs a collection of the images that are suitable for diagnosing the specified disease. [Effects of the Invention]

[0009] According to the above, it is possible to provide a computer program, an information processing method, and an information processing device that can select and collect images suitable for diagnosing a predetermined disease from a series of multiple images obtained by a scanning probe that scans an organ of a subject, and output the amount of collected images. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a schematic diagram illustrating an example of the configuration of an ultrasound diagnostic apparatus according to Embodiment 1. FIG. [Figure 2] 1 is a block diagram showing an example of the configuration of an information processing device according to a first embodiment. [Figure 3]1 is a functional block diagram showing an example of the configuration of an information processing device according to a first embodiment. [Figure 4] FIG. 2 is a block diagram showing an example of the configuration of an appropriateness learning model according to the first embodiment. [Figure 5] FIG. 2 is a block diagram showing an example of the configuration of an individual learning model. [Figure 6] FIG. 1 is a schematic diagram showing an example of an echo image of a normal lung suitable for lung diagnosis. [Figure 7A] 1A and 1B are schematic diagrams showing examples of an echo image of an abnormal lung suitable for lung diagnosis and an echo image that is inappropriate for lung diagnosis. [Figure 7B] 1A and 1B are schematic diagrams showing examples of an echo image of an abnormal lung suitable for lung diagnosis and an echo image that is inappropriate for lung diagnosis. [Figure 7C] 1A and 1B are schematic diagrams showing examples of an echo image of an abnormal lung suitable for lung diagnosis and an echo image that is inappropriate for lung diagnosis. [Figure 8] FIG. 1 is a block diagram showing an example of the configuration of an integrated learning model. [Figure 9] 4 is a flowchart showing an information processing procedure according to the first embodiment. [Figure 10] FIG. 2 is a schematic diagram showing an example of an ultrasound diagnostic monitor screen. [Figure 11] FIG. 10 is an explanatory diagram showing a method for detecting a B line. [Figure 12] FIG. 10 is a block diagram showing an example of the configuration of an index learning model according to the second embodiment. [Figure 13] FIG. 11 is a block diagram showing an example of the configuration of an index learning model according to a third embodiment. [Figure 14] FIG. 10 is a schematic diagram illustrating an example of the configuration of an ultrasound diagnostic apparatus according to a fourth embodiment. [Figure 15] 10 is a flowchart showing an information processing procedure according to the fourth embodiment. [Figure 16] FIG. 2 is a schematic diagram showing a predetermined scanning region. [Figure 17] 10 is a flowchart showing an information processing procedure according to the fifth embodiment. [Figure 18] FIG. 10 is a schematic diagram illustrating an example of the configuration of an ultrasound diagnostic apparatus according to a sixth embodiment. [Figure 19]13 is a flowchart showing an information processing procedure according to the sixth embodiment. [Figure 20] FIG. 20 is a block diagram showing an example of the configuration of an appropriateness learning model according to a seventh embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Specific examples of a computer program, an information processing method, and an information processing device according to embodiments of the present invention will be described below with reference to the drawings. Note that the present invention is not limited to these examples, but is defined by the claims, and is intended to include all modifications within the meaning and scope equivalent to the claims. Furthermore, at least some of the embodiments described below may be combined in any manner.

[0012] 1 is a schematic diagram illustrating an example of the configuration of an ultrasound diagnostic apparatus according to embodiment 1. The ultrasound diagnostic apparatus according to embodiment 1 includes an information processing device 1 and an ultrasound probe 2. The information processing device 1 and the ultrasound probe 2 are wirelessly connected, and can transmit and receive various signals. Alternatively, the ultrasound probe 2 may be connected to the information processing device 1 via a wired cable.

[0013] The ultrasound probe 2 is a device that scans the subject's organs with ultrasound, and ultrasound scanning is controlled by the information processing device 1. The ultrasound probe 2 includes, for example, multiple piezoelectric elements, an acoustic matching layer, and an acoustic lens. The piezoelectric elements generate ultrasound waves in response to a drive signal output from the information processing device 1. The ultrasound waves generated by the piezoelectric elements are transmitted from the ultrasound probe 2 to the subject's living body via the acoustic matching layer and acoustic lens. The acoustic matching layer is a component that matches the acoustic impedance between the piezoelectric elements and the subject. The acoustic lens is an element that focuses the ultrasound waves propagating from the piezoelectric elements and transmits them to the subject. The ultrasound waves transmitted from the ultrasound probe 2 to the subject are reflected by discontinuous surfaces in the subject's organs where the acoustic impedance is discontinuous and received by multiple piezoelectric elements. The amplitude of the reflected wave depends on the difference in acoustic impedance at the reflecting surface. The arrival time of the reflected wave depends on the depth of the reflecting surface. The piezoelectric elements convert the vibration pressure of the reflected ultrasound waves into an electrical signal. Hereinafter, this electrical signal will be referred to as an echo signal. The ultrasound probe 2 outputs the echo signal to the information processing device 1.

[0014] 2 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 including a control unit 11, a memory 12, a storage unit 13, an operation unit 14, a display unit 15, and a communication unit 16. 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.

[0015] The control unit 11 is an arithmetic processing device such as one or more central processing units (CPUs), micro-processing units (MPUs), graphics processing units (GPUs), general-purpose computing on graphics processing units (GPGPUs), tensor processing units (TPUs), etc. The control unit 11 reads and executes a computer program 131 stored in the storage unit 13 to control ultrasonic scanning by the ultrasonic probe 2, sequentially generate a series of multiple echo images in real time based on signals obtained from the ultrasonic probe 2, determine whether the generated multiple echo images are suitable for diagnosing a predetermined pulmonary disease (predetermined disease), display the collection amount and target collection amount of echo images suitable for diagnosing the predetermined pulmonary disease in real time in parallel with the process of generating the echo images, and, when the number of echo images collected exceeds the target collection amount, calculate and display an index for diagnosing the predetermined pulmonary disease based on the echo images.

[0016] The communication unit 16 includes a processing circuit, a communication circuit, etc. for wireless communication processing, and transmits and receives various signals to and from the ultrasonic probe 2. Specifically, the communication unit 16 generates ultrasonic waves by transmitting a drive signal to the ultrasonic probe 2 under the control of the control unit 11. Then, the communication unit 16 receives the echo signal output from the ultrasonic probe 2.

[0017] The memory 12 is a volatile memory such as a DRAM (Dynamic RAM) or an SRAM (Static RAM), and temporarily stores a computer program 131 read from the storage unit 13 when the control unit 11 executes arithmetic processing, or various data generated by the arithmetic processing of the control unit 11.

[0018] The storage unit 13 is a storage device such as a hard disk, an EEPROM (Electrically Erasable Programmable ROM), a flash memory, etc. The storage unit 13 stores a computer program 131 and an appropriateness learning model 17 required by the control unit 11 to collect echo images and perform diagnostic processing for predetermined lung diseases.

[0019] The computer program 131 is a program for causing a computer to function as the information processing device 1 according to the present embodiment 1. The computer program 131 causes the computer to execute the information processing method according to the present embodiment 1, such as collecting echo images and diagnosing a predetermined lung disease.

[0020] The computer program 131 may be recorded in a computer-readable manner on the recording medium 10. The storage unit 13 stores the computer program 131 read from the recording medium 10 by a reading device (not shown). The recording medium 10 is a semiconductor memory such as a flash memory, an optical disk, a magnetic disk, a magneto-optical disk, or the like. Alternatively, the computer program 131 according to this embodiment may be downloaded from an external server (not shown) connected to a communication network and stored in the storage unit 13.

[0021] The operation unit 14 is an input device that accepts operations by an operator using the ultrasound diagnostic apparatus. The operator may be, for example, a medical professional such as a doctor, a laboratory technician, or a nurse. The input device may be, for example, a pointing device such as a touch panel, or a keyboard.

[0022] The display unit 15 is an output device that outputs information such as an echo image, an echo image collection level, a pulmonary congestion level, etc. The output device is, for example, a liquid crystal display or an EL display.

[0023] 3 is a functional block diagram showing an example of the configuration of the information processing device 1 according to embodiment 1. The control unit 11 of the information processing device 1 reads and executes a computer program 131 stored in the storage unit 13, thereby functioning as a probe control unit 11a, an image generation unit 11b, a pulmonary diagnosis adequacy determination unit 11c, a pulmonary diagnostic image storage unit 11d, a pulmonary diagnostic image collection level display processing unit 11e, a pulmonary congestion level calculation unit 11f, and a pulmonary congestion level display processing unit 11g.

[0024] The probe control unit 11a controls the ultrasonic scanning process by the ultrasonic probe 2. Specifically, it outputs a drive signal from the ultrasonic probe 2 to generate ultrasonic waves, and receives echo signals output from the ultrasonic probe 2.

[0025] The image generator 11b executes a process of generating an echo image based on the echo signal received by the communication unit 16. The image generator 11b generates a series of echo images in real time every time the communication unit 16 receives an echo signal. The echo image is, for example, a B-mode image in which the intensity of the reflected wave is represented by brightness, and a two-dimensional tomographic image of the organ is reproduced. Note that the type of echo image is not particularly limited.

[0026] The lung diagnosis appropriateness determining unit 11c executes a process of determining whether the generated echo image is an image appropriate for diagnosing a predetermined lung disease, for example, pulmonary congestion. In the following description, the predetermined lung disease is assumed to be pulmonary congestion.

[0027] The lung diagnostic image storage unit 11d executes a process of storing the echo images that have been determined to be suitable for diagnosing pulmonary congestion.

[0028] The pulmonary diagnostic image collection level display processing unit 11e executes processing to display the collection level of echo images suitable for diagnosing pulmonary congestion and the target collection volume required to calculate the pulmonary congestion level on the display unit 15. The pulmonary diagnostic image collection level display processing unit 11e calculates the collection volume of echo images in real time and displays it on the display unit 15.

[0029] When the number of echo images collected is equal to or greater than the target collection amount, the pulmonary congestion degree calculation unit 11f executes a process of calculating the pulmonary congestion degree based on the echo images.

[0030] The pulmonary congestion level display processing unit 11g executes a process of displaying on the display unit 15 the pulmonary congestion level calculated by the pulmonary congestion level calculation unit 11f.

[0031] FIG. 4 is a block diagram showing an example configuration of the appropriateness learning model 17 according to the first embodiment. The appropriateness learning model 17 includes a plurality of individual learning models 171 and an integrated learning model 172. A plurality of echo images are input to each of the plurality of individual learning models 171. Each individual learning model 171 is a learning model that extracts feature amounts of the input echo images and outputs the extracted feature amounts to the integrated learning model 172. The integrated learning model 172 is input with the feature amounts of the echo images output from the plurality of individual learning models 171. The integrated learning model 172 is a learning model that, when feature amounts of a plurality of echo images are input, outputs an image appropriateness indicating the degree to which the plurality of echo images are appropriate for diagnosing pulmonary congestion.

[0032] FIG. 5 is a block diagram showing an example configuration of the individualized learning model 171. The individualized learning model 171 is a trained model that has been trained to extract and output feature quantities related to the diagnosis of pulmonary congestion from an echo image through machine learning using teacher data and unsupervised learning using an autoencoder. The individualized learning model 171 performs a predetermined calculation on input values ​​and outputs the calculation results, and data such as coefficients and thresholds of functions that define this calculation are stored in the memory unit 13 as the individualized learning model 171. By reading the data stored as the individualized learning model 171, the control unit 11 is able to execute calculation processing to extract features of the echo image. In this embodiment 1, the learning process of the individual learning model 171 is performed by a learning computer. Note that, like the computer program 131, data related to the learned individual learning model 171 may be provided in the form of distribution via a communication network, or may be provided in the form of being recorded on a recording medium 10.

[0033] In the first embodiment, the individualized learning model 171 is, for example, a neural network having an input layer 171a to which an echo image is input and an intermediate layer 171b that extracts features of the echo image. The individualized learning model 171 is configured using, for example, an autoencoder. As shown in FIG. 5, the autoencoder includes an input layer 171a to which an echo image is input, a first intermediate layer 171b that dimensionally compresses the input image and extracts features, a second intermediate layer 171c that restores the echo image from the extracted features, and an output layer 171d that outputs the restored echo image. The first intermediate layer 171b and the second intermediate layer 171c are also referred to as a convolution layer and a deconvolution layer. The individualized learning model 171 is configured with an input layer 171a to which an echo image of the encoder is input and an intermediate layer 171b that dimensionally compresses the input image and extracts features. 5, the second hidden layer 171c and output layer 171d, which are drawn with dashed lines, are not essential components of the individual learning model 171. Note that the configuration of the individual learning model 171 is not particularly limited as long as it is configured so that features of the echo image can be extracted and provided to the subsequent integrated learning model 172, and the above autoencoder may be configured as the individual learning model 171 as is. An example of constructing an individualized learning model 171 using the input layer 171a and first hidden layer 171b of an autoencoder will be described below.

[0034] The input layer 171a of the neural network has a plurality of neurons to which the pixel values ​​of the pixels of the echo image are input, and passes each input data to the intermediate layer 171b.

[0035] The first hidden layer 171b has multiple layers each consisting of multiple neurons. The hidden layer 171b is a layer that compresses the dimensionality of image data. For example, the hidden layer 171b performs convolution processing to compress the dimensionality of an echo image. Through dimensional compression, each layer extracts features of the echo image of normal lungs and features of the echo image of abnormal lungs from the input data and passes them on sequentially from the previous layer to the subsequent layer. The final layer of the first hidden layer 171b outputs the features extracted from the echo image. Although the meaning of the features cannot be immediately interpreted, it is believed that image features that appear in normal and abnormal lungs, such as the presence or absence of a real image, A-lines 31, B-lines 33, ground-glass opacity 34, and bat sign 32, are related.

[0036] The following describes a learning method for the individual learning model 171. First, an autoencoder before learning is prepared. The autoencoder includes an input layer 171a, a first hidden layer 171b, a second hidden layer 171c, and an output layer 171d. The computer collects multiple echo images of normal lungs and multiple echo images of abnormal lungs. That is, it collects multiple echo images suitable for diagnosing a predetermined lung disease. Next, the computer uses the collected echo images to perform machine learning or deep learning on the pre-learning autoencoder so that the echo images input to the input layer 171a and the images output from the output layer 171d are the same. Specifically, the computer inputs multiple echo images, which are training data, into a pre-training autoencoder, and acquires an image output from the output layer 171d after arithmetic processing in the first and second intermediate layers 171b and 171c. The computer then compares the image output from the output layer 171d with the input echo image and optimizes parameters used in the arithmetic processing in the intermediate layers 171b and 171c so that the image output from the output layer 171d approaches the input echo image. The parameters include, for example, weights (coupling coefficients) between neurons. While the parameter optimization method is not particularly limited, the computer optimizes various parameters using, for example, the steepest descent method. The computer then generates the individualized training model 171 by extracting the input layer 171a and the first intermediate layer 171b from the trained autoencoder.

[0037] Although supervised learning has been exemplified above, the individual learning model 171 may be generated by supervised learning using a CNN (Convolutional Neural Network). Also, although an example in which the individual learning model 171 is a neural network has been described, it may be a model configured as an SVM (Support Vector Machine), a Bayesian network, a regression tree, or the like.

[0038] FIG. 6 is a schematic diagram showing an example of an echo image of a normal lung suitable for lung diagnosis. An appropriate echo image of a normal lung includes a clear A-line 31. The A-line 31 is an image resulting from multiple reflections occurring between the pleura and the ultrasound probe 2. An appropriate echo image of a normal lung in a sagittal plane also includes an image known as the Batt sign 32. The Batt sign 32 is a curved, convex image obtained when ultrasound is reflected by the ribs. These A-line 31 and Batt sign 32 are features contained in an echo image suitable for diagnosing a specific lung disease (diagnosing the lungs as normal).

[0039] 7A to 7C are schematic diagrams showing examples of echo images of abnormal lungs suitable for lung diagnosis and unsuitable echo images. FIG. 7A is a schematic diagram of an echo image of abnormal lungs in which B-lines 33 are visible, and FIG. 7B is a schematic diagram of an echo image of abnormal lungs in which ground-glass opacity 34 is visible. FIG. 7C is an echo image without a solid image, which is unsuitable for diagnosing a specific lung disease. B-lines 33 are images caused by thickening of interlobular septa or accumulation of fluid in the alveoli. Ground-glass opacity 34 is an image caused by abnormal lungs such as pneumonia. These B-lines 33 and ground-glass opacity 34 are features contained in echo images suitable for diagnosing a specific lung disease.

[0040] FIG. 8 is a block diagram showing an example of the configuration of the integrated learning model 172. The integrated learning model 172 is a trained model that has been trained through unsupervised learning, such as machine learning using teacher data and clustering, to output an image suitability indicating the degree to which a plurality of echo images are suitable for diagnosing a predetermined lung disease, based on the feature quantities of the echo images. The integrated learning model 172 performs a predetermined calculation on input values ​​and outputs the calculation results. The memory unit 13 stores data such as coefficients and thresholds of functions that define this calculation as the integrated learning model 172. By reading the data stored as the integrated learning model 172, the control unit 11 can execute calculation processing to determine the suitability of an echo image based on the feature quantities of the echo image. In this embodiment 1, the learning process of the integrated learning model 172 is performed by a learning computer. Data related to the trained integrated learning model 172 may be provided in the form of distribution via a communication network, similar to the computer program 131, or may be provided in the form of being recorded on a recording medium 10.

[0041] In this embodiment 1, the integrated learning model 172 is a neural network having, for example, an input layer 172a to which features of multiple echo images are input, an intermediate layer 172b that extracts the features of the echo images, and an output layer 172c that outputs the extracted features.

[0042] The input layer 172a of the neural network has a plurality of neurons to which feature quantities of a plurality of echo images are input, and passes each input data to the intermediate layer 172b.

[0043] The intermediate layer 172b has multiple layers each made up of multiple neurons. Each layer extracts features related to the appropriateness of the echo image from the input data and passes them on to the subsequent layers in order, until the final layer passes them on to the output layer 172c.

[0044] The output layer 172c includes neurons that output the calculation results, and the neurons output image suitability indicating the degree to which the multiple echo images are suitable for diagnosing a predetermined lung disease.

[0045] In the first embodiment, an example has been described in which the integrated learning model 172 is a neural network, but it may also be a model configured as an SVM (Support Vector Machine), a Bayesian network, a regression tree, or the like.

[0046] The learning method of the integrated learning model 172 will be described below. First, the computer collects feature quantities of multiple echo images that serve as the basis for training data. Then, the computer generates training data by assigning training data indicating whether the images are suitable for diagnosing a predetermined lung disease to the multiple feature quantities. Next, the computer uses the generated training data to perform machine learning or deep learning on the pre-training neural network model, thereby generating the integrated learning model 172. Specifically, the computer inputs feature quantities of multiple echo images included in the training data into a pre-training neural network model, and after arithmetic processing in the intermediate layer 172b, acquires an image appropriateness level output from the output layer 172c. The computer then compares the image appropriateness level output from the output layer 172c with the image appropriateness level indicated by the training data, and optimizes parameters used in the arithmetic processing in the intermediate layer 172b so that the image appropriateness level output from the output layer 172c approaches a correct value. The parameters in question are, for example, weights (coupling coefficients) between neurons. The method for optimizing the parameters is not particularly limited, and the computer may optimize various parameters using, for example, the steepest descent method.

[0047] The information processing device 1 obtains a trained integrated learning model 172 by repeatedly performing the above process based on the training data of a large number of patients included in the training data.

[0048] Fig. 9 is a flowchart showing an information processing procedure according to the first embodiment, and Fig. 10 is a schematic diagram showing an example of an ultrasound diagnostic monitor screen. The control unit 11 of the information processing device 1 displays a monitor screen as shown in Fig. 10 on the display unit 15 (step S111). The monitor screen includes an echo image display unit 151, a collection level gauge 152, a lung diagnostic image display unit 153, a congestion level display unit 154, a start button 155, a stop button 156, etc. The echo image display unit 151 displays echo images generated based on the echo signals in real time. The collection level gauge 152 displays the collection level of echo images suitable for diagnosing a predetermined lung disease among the generated echo images. The collection level gauge 152 displays the target collection volume of echo images required for diagnosing a predetermined lung disease using a predetermined number of meter blocks 152a. When the number of echo images corresponding to one meter block 152a has been collected, the collection level gauge 152 displays the collection volume of echo images by changing the color of the meter blocks 152a, starting from the bottom. When the color of all meter blocks 152a has changed, the collection volume has reached the target collection volume. The lung diagnostic image display unit 153 displays representative echo images suitable for diagnosing a predetermined lung disease. The congestion level display unit 154 displays the diagnosis result of the predetermined lung disease. The start button 155 is an operation button for starting the collection of echo images and the diagnostic process for the predetermined lung disease, and the stop button 156 is an operation button for stopping the process.

[0049] Next, the control unit 11 receives the echo signals output from the ultrasonic probe 2 and generates an echo image based on the received echo signals (step S112).Then, the control unit 11 displays the generated echo signals on the echo image display unit 151 (step S113).

[0050] Next, the control unit 11 calculates the image suitability by inputting each of the multiple echo images into the individual learning model 171 (step S114), and determines whether the multiple echo images are suitable for diagnosing a predetermined lung disease based on the calculated image suitability (step S115). If the control unit 11 determines that the multiple echo images are unsuitable (step S115: NO), the control unit 11 returns the process to step S112.

[0051] If it is determined that the image is suitable for diagnosing the predetermined lung disease (step S115: YES), the control unit 11 stores the echo image determined to be suitable in the storage unit 13 (step S116). That is, the control unit 11 collects the echo image.

[0052] Next, the control unit 11 calculates the collection amount of echo images suitable for diagnosing the predetermined lung disease (step S117) and displays the calculated collection amount on the collection level gauge 152 (step S118). The control unit 11 that displays the collection amount functions as an output unit that outputs the collection amount of the images suitable for diagnosing the predetermined disease. Furthermore, the control unit 11 displays a representative echo image suitable for lung diagnosis as a sample on the lung diagnostic image display unit 153 (step S119).

[0053] Next, the control unit 11 determines whether the amount of echo images collected has reached a predetermined target amount (step S120). If it is determined that the target amount has not been reached (step S120: NO), the control unit 11 returns the process to step S112.

[0054] If it is determined that the target collection amount has been reached (step S120: YES), the control unit 11 calculates the pulmonary congestion degree (step S121), displays the calculated pulmonary congestion degree on the congestion degree display unit 154 (step S122), and ends the processing.

[0055] Fig. 11 is an explanatory diagram showing a method for detecting the B-line 33. The left diagram in Fig. 11 is an echo image generated based on the echo signal. Note that this is a polar coordinate image in which the horizontal axis indicates the depth direction and the vertical axis indicates the angle indicating the direction in which the ultrasound is transmitted. The brightness of each pixel corresponds to the amplitude of the echo signal.

[0056] The control unit 11 integrates the brightness value of each pixel in the depth direction for the echo image. The diagram in the center of Fig. 11 is a graph conceptually showing the integration results. The horizontal axis represents the angle, and the vertical axis represents the integrated value.

[0057] Next, the control unit 11 differentiates the integral value with respect to the angle direction. The right diagram in FIG. 11 is a graph conceptually illustrating the differentiation result. The horizontal axis represents the angle, and the vertical axis represents the differential value. The control unit 11 determines that a location where the differential value is equal to or greater than a predetermined value is a B-line 33. The control unit 11 then counts the number of echo images in which a B-line 33 is present among the multiple collected echo images, and determines that pulmonary congestion exists if a predetermined percentage or more, for example, 37.5% or more, of the echo images contain a B-line 33.

[0058] When the control unit 11 determines that there is pulmonary congestion, it displays, for example, the proportion of B-lines 33 present on the congestion level display unit 154. When the control unit 11 determines that there is pulmonary congestion, it displays on the congestion level display unit 154 that there are no findings.

[0059] As described above, the ultrasound diagnostic apparatus according to the first embodiment can select and collect images suitable for diagnosing a predetermined lung disease from a series of multiple images obtained by a scanning probe that scans the organs of a subject, and can output the amount of collected images.

[0060] In addition, the acquisition level and target acquisition level of images suitable for diagnosing a predetermined lung disease can be displayed in real time.

[0061] Furthermore, images suitable for diagnosing certain lung diseases can be displayed.

[0062] Furthermore, when the collection level of images suitable for diagnosing a predetermined lung disease reaches a target collection level, the degree of pulmonary congestion can be calculated and displayed based on the collected images.

[0063] Although the first embodiment has been described with reference to an example in which the lungs of a subject are scanned, the present invention can also be applied to the case of scanning other organs. Furthermore, the first embodiment has been described with reference to an example in which an organ of a subject is scanned using ultrasound, but the present invention can also be applied to the case in which a scanning probe that optically acquires a tomographic image of the organ, such as a probe for optical coherence tomography diagnosis, is used.

[0064] Furthermore, in the first embodiment, an example has been described in which the display unit 15 of the information processing device 1 constituting the ultrasound diagnostic device displays the collection level and target collection level of images suitable for diagnosing a predetermined pulmonary disease, scanned images, representative images suitable for diagnosing a predetermined pulmonary disease, etc. However, the information processing device 1 may be configured to output and display these various types of information on an external monitor device.

[0065] Furthermore, in this embodiment 1, an example has been described in which multiple individual learning models 171 and integrated learning model 172 are used to determine whether an image is suitable for diagnosing a specified lung disease, but it may also be configured to use a single learning model to individually determine the suitability of multiple images.

[0066] (Embodiment 2) The ultrasound diagnostic device according to embodiment 2 differs from embodiment 1 in that it uses a learning model to diagnose a predetermined lung disease based on the feature amount of an echo image. Since the other configurations of the information processing device 1 are the same as those of the information processing device 1 according to embodiment 1, the same reference numerals are used for the same parts and detailed description will be omitted.

[0067] FIG. 12 is a block diagram showing an example of the configuration of an index learning model 218 according to the second embodiment. The storage unit 13 of the information processing device 1 according to the second embodiment stores the index learning model 218. The index learning model 218 is a trained model that has been trained to output the degree of pulmonary congestion from the feature quantities of an echo image by unsupervised learning, such as machine learning using teacher data and clustering. The feature quantities of the echo image include, for example, the presence or absence of B-lines 33, the number of B-lines 33, the presence or absence of ground-glass opacity 34, and the contrast of the bat sign 32. The index learning model 218 performs a predetermined calculation on input values ​​and outputs the calculation results. The storage unit 13 stores data, such as coefficients and thresholds of functions that define this calculation, as the index learning model 218. By reading the data stored as the index learning model 218, the control unit 11 can execute calculation processing to calculate the degree of pulmonary congestion from the feature quantities of the echo image. In the second embodiment, the learning process of the index learning model 218 is performed by a learning computer. Data related to the learned index learning model 218 may be provided in the form of distribution via a communication network, similar to the computer program 131, or may be provided in the form of being recorded on the recording medium 10.

[0068] In this embodiment 2, the index learning model 218 is a neural network having, for example, an input layer 218a to which features of an echo image are input, an intermediate layer 218b that extracts the features of the echo image, and an output layer 218c that outputs the extracted features.

[0069] The input layer 218a of the neural network has a plurality of neurons to which the feature quantities of the echo image are input, and passes each input data to the intermediate layer 218b.

[0070] The intermediate layer 218b has multiple layers each made up of multiple neurons. Each layer extracts features of pulmonary congestion from the input data and passes them on to the subsequent layers in order, until the final layer passes them on to the output layer 218c.

[0071] The output layer 218c includes a neuron that outputs the pulmonary congestion degree.

[0072] In the second embodiment, an example has been described in which the index learning model 218 is a neural network, but it may also be a model configured as an SVM (Support Vector Machine), a Bayesian network, a regression tree, or the like.

[0073] The learning method of the index learning model 218 will be described below. First, the computer collects feature quantities of multiple echo images that serve as the source of training data. Then, the computer generates training data by assigning training data indicating the degree of pulmonary congestion to the feature quantities of the echo images. Next, the computer generates the index learning model 218 by using the generated training data to perform machine learning or deep learning on the pre-training neural network model. Specifically, the computer inputs feature quantities of multiple echo images included in the training data into a pre-training neural network model, and after arithmetic processing in the intermediate layer 218b, obtains the pulmonary congestion level output from the output layer 218c. The computer then compares the pulmonary congestion level output from the output layer 218c with the pulmonary congestion level indicated by the training data, and optimizes parameters used in the arithmetic processing in the intermediate layer 218b so that the pulmonary congestion level output from the output layer 218c approaches the correct value. The parameters include, for example, weights (coupling coefficients) between neurons. The method for optimizing the parameters is not particularly limited, and the computer may optimize various parameters using, for example, the steepest descent method.

[0074] The information processing device 1 obtains a trained index learning model 218 by repeatedly performing the above process based on the training data of a large number of patients included in the training data.

[0075] By inputting the feature quantities of the echo image determined to be suitable for diagnosing a pulmonary disease into the index learning model 218 configured in this manner, the degree of pulmonary congestion can be calculated. The feature amounts of the echo image can be calculated using, for example, a learning model (not shown). The learning model is, for example, a neural network having an input layer to which the echo image is input, an intermediate layer that extracts the feature amounts of the echo image, and an output layer. The learning model is a CNN (Convolutional Neural Network) and includes multiple convolution layers, pooling layers, fully connected layers, etc. The feature amounts include, for example, the presence or absence of a real image, the presence or absence of an A-line 31, the presence or absence of a B-line 33, the number of B-lines 33, the presence or absence of a ground-glass opacity 34, the presence or absence of a bat sign 32, etc. The above-mentioned contents of each neuron and output data are merely examples and are not particularly limited. The learning method for the learning model will be described below. First, the computer collects multiple echo images that serve as the source of training data. Then, the computer generates training data by adding training data indicating feature quantities such as the presence or absence of a real image and the presence or absence of a B-line 33 to the multiple echo images. Next, the computer uses the generated training data to perform machine learning or deep learning on the pre-training neural network model, thereby generating an individualized learning model 171. Specifically, the computer inputs the echo images included in the training data into a pre-training neural network model, performs arithmetic processing in the intermediate layer, and obtains feature values ​​output from the output layer. The computer then compares the feature values ​​output from the output layer with the feature values ​​indicated by the training data, and optimizes the parameters used in the arithmetic processing in the intermediate layer so that the data output from the output layer approaches the correct value.

[0076] As described above, according to the ultrasound diagnostic device of the second embodiment, the degree of pulmonary congestion can be calculated by inputting features of the echo image, such as the presence or absence of B-lines 33, the number of B-lines 33, the presence or absence of ground-glass opacity 34, and the contrast of the bat sign 32, into the index learning model 218.

[0077] (Embodiment 3) The ultrasound diagnostic device according to embodiment 3 differs from embodiment 1 in that the ultrasound image itself is input into a learning model to diagnose a predetermined lung disease. Since the other configurations of the information processing device 1 are the same as those of the information processing device 1 according to embodiment 2, the same reference numerals are used for the same parts and detailed description will be omitted.

[0078] 13 is a block diagram showing a configuration example of an index learning model 318 according to embodiment 3. The storage unit 13 of the information processing device 1 according to embodiment 3 stores the index learning model 318. The index learning model 318 is a neural network having, for example, an input layer 318a to which an echo image is input, an intermediate layer 318b that extracts the echo image, and an output layer 318c that outputs the extracted feature amount.

[0079] The input layer 318a of the neural network has a plurality of neurons to which the pixel values ​​of each pixel of the echo image are input, and passes each input data to the intermediate layer 318b.

[0080] The intermediate layer 318b has multiple layers each consisting of multiple neurons. For example, the indicator learning model 318 in the third embodiment is a CNN, which includes multiple convolutional layers, pooling layers, fully connected layers, etc. Each layer extracts features of pulmonary congestion from input data and passes them on to the subsequent layer in order, and the final layer passes them on to the output layer 318c.

[0081] The output layer 318c includes a neuron that outputs the pulmonary congestion degree.

[0082] The method for generating the index learning model 318 is the same as in embodiment 2. By inputting an echo image determined to be suitable for diagnosing a pulmonary disease into the index learning model 318 configured in this manner, the degree of pulmonary congestion can be calculated.

[0083] As described above, according to the ultrasound diagnostic apparatus of the third embodiment, the echo image is input to the index learning model 318, whereby the degree of pulmonary congestion can be calculated.

[0084] (Embodiment 4) The ultrasound diagnostic device according to embodiment 4 differs from embodiment 1 in that it generates a predetermined number or more of echo images at a plurality of predetermined scanning regions to diagnose pulmonary diseases. Since the other configurations of the information processing device 1 are the same as those of the information processing device 1 according to embodiment 1, the same reference numerals are used for the same parts and detailed description thereof will be omitted.

[0085] 14 is a schematic diagram illustrating an example of the configuration of an ultrasound diagnostic apparatus according to embodiment 4. The ultrasound probe 2 according to embodiment 4 includes an acceleration sensor 421 (positioning sensor) and outputs an acceleration signal to the information processing device 1. The information processing device 1 receives the acceleration signal output from the ultrasound probe 2 and estimates the position of the ultrasound probe 2 based on the received acceleration signal. Specifically, the information processing device 1 uses the position of the ultrasound probe 2 relative to the subject when the ultrasound sensor is started as a reference position and estimates the position of the ultrasound probe 2 relative to the reference position based on the acceleration signal.

[0086] 15 is a flowchart showing an information processing procedure according to embodiment 4. The control unit 11 of the information processing device 1 displays a monitor screen in the same procedure as steps S111 to S113 of embodiment 1 (step S411), generates an echo image (step S412), and displays the generated echo image on the display unit 15 (step S413).

[0087] Next, the control unit 11 receives the acceleration signal output from the ultrasonic probe 2, and estimates the position of the ultrasonic probe 2 relative to the scanning region, that is, the subject, based on the received acceleration information (step S414).

[0088] FIG. 16 is a schematic diagram showing predetermined scanning regions. In the fourth embodiment, as shown in FIG. 16, it is assumed that four scanning regions (regions indicated by numbers "1," "2," "3," and "4" in FIG. 16) obtained by dividing the right lung into upper, lower, left, and right sections and four scanning regions (regions indicated by numbers "5," "6," "7," and "8" in FIG. 16) obtained by dividing the left lung into upper, lower, left, and right sections are scanned in numerical order. The information processing device 1 estimates the position of the ultrasound probe 2 when the start button 155 on the monitor screen is first operated and the first real image begins to be obtained as scanning region "1." Thereafter, the control unit 11 calculates the position of the ultrasound probe 2 relative to scanning region "1" based on the acceleration signal, and estimates the position of the ultrasound probe 2 relative to the subject.

[0089] Next, the control unit 11 calculates the amount of echo images generated in the predetermined plurality of scanning regions (step S415), and determines whether or not a predetermined amount of echo images or more have been generated in each region (step S416).

[0090] If it is determined that there is a scanning region where the amount of echo images generated is less than the predetermined amount (step S416: YES), the control unit 11 displays on the display unit 15 an instruction image (movement instruction information) indicating that the ultrasonic probe 2 should be moved to a scanning region where the amount of echo images generated is less than the predetermined amount (step S417). Note that if the ultrasonic probe 2 is currently located at a scanning region where the amount of echo images generated is less than the predetermined amount, the instruction image is not displayed. Also, if there are multiple regions where the amount of echo images generated is less than the predetermined amount, it is preferable to configure the system to instruct the user to move to a scanning region with a smaller number in a predetermined order, for example, from "1" to "8".

[0091] When the process of step S417 is completed, or when it is determined in step S416 that there are no scans less than the predetermined amount (step S416: NO), the control unit 11 determines whether the generated echo images are suitable for diagnosing a predetermined lung disease by inputting each of the multiple echo images into the individual learning model 171 (step S114). The process from step S114 onward is the same as in embodiment 1, and therefore details will be omitted.

[0092] As described above, the ultrasound diagnostic apparatus according to the fourth embodiment can scan every part of the lungs without omission, generate and collect echo images, and accurately diagnose lung diseases.

[0093] (Embodiment 5) The ultrasound diagnostic device according to embodiment 5 differs from embodiment 1 in that it can instruct the surgeon on the position of the ultrasound probe 2 so that echo images suitable for diagnosing lung diseases are efficiently collected. Since the other configurations of the information processing device 1 are the same as those of the information processing device 1 according to embodiment 1, the same reference numerals are used for the same parts and detailed description will be omitted.

[0094] The ultrasonic probe 2 according to the fifth embodiment includes an acceleration sensor 421 (attitude sensor) similar to the fourth embodiment, and outputs an acceleration signal to the information processing device 1. The information processing device 1 receives the acceleration signal output from the ultrasonic probe 2, and estimates the attitude of the ultrasonic probe 2 based on the received acceleration signal.

[0095] 17 is a flowchart showing an information processing procedure according to embodiment 5. The control unit 11 of the information processing device 1 displays a monitor screen in the same procedure as steps S111 to S113 of embodiment 1 (step S511), generates an echo image (step S512), and displays the generated echo image on the display unit 15 (step S513).

[0096] Next, the control unit 11 receives the acceleration signal output from the ultrasonic probe 2, and estimates the posture of the ultrasonic probe 2 relative to the subject based on the received acceleration information (step S514).

[0097] Next, the control unit 11 inputs each of the generated multiple echo images into the individual learning model 171 to calculate an image suitability indicating the degree to which the echo image is suitable for diagnosing the specified lung disease (step S515), and determines whether the echo image is suitable for diagnosing the specified lung disease based on the calculated image suitability (step S516).

[0098] If it is determined that the image is suitable for diagnosing a specified lung disease (step S516: YES), the control unit 11 stores the echo image determined to be suitable in the memory unit 13 (step S517) and stores posture information indicating the posture of the ultrasound probe 2 (step S518).

[0099] Thereafter, similarly to steps S117 to S120 in embodiment 1, the control unit 11 calculates the collection amount of echo images (step S519), displays it on the collection amount gauge 152 (step S520), and displays a representative echo image suitable for lung diagnosis as a sample on the lung diagnostic image display unit 153 (step S521). The subsequent processing is similar to that in embodiment 1, and therefore will not be repeated in detail.

[0100] If it is determined in step S516 that the echo image is not suitable for diagnosing the predetermined pulmonary disease (step S516: NO), the control unit 11 determines whether the amount of generated echo images suitable for diagnosing the predetermined pulmonary disease has decreased (step S523). If it is determined that the amount of generated echo images suitable for diagnosing the predetermined pulmonary disease has decreased (step S523: YES), the control unit 11 reads out the posture information of the ultrasound probe 2 stored in the storage unit 13, displays the read posture information or an instruction image (posture change instruction information) instructing a posture change based on the posture information on the display unit 15 (step S524), and returns the process to step S512. If it is determined that the amount of generated echo images suitable for diagnosing the predetermined pulmonary disease has not decreased (step S523: NO), the control unit 11 returns the process to step S512.

[0101] As described above, the ultrasonic diagnostic apparatus according to the fifth embodiment can instruct the operator to position the ultrasonic probe 2 so that an echo image suitable for diagnosing a predetermined lung disease is generated.

[0102] (Embodiment 6) The ultrasound diagnostic apparatus according to embodiment 6 differs from embodiment 1 in that it collects echo images taking into account the respiratory cycle of the subject. Since the other configurations of the information processing apparatus 1 are the same as those of the information processing apparatus 1 according to embodiment 1, the same reference numerals are used for the same parts and detailed description will be omitted.

[0103] 18 is a schematic diagram illustrating an example of the configuration of an ultrasound diagnostic apparatus according to embodiment 6. The ultrasound diagnostic apparatus according to embodiment 6 includes a respiratory cycle sensor 603. The respiratory cycle sensor 603 is, for example, a PPG (Photoplethysmography) pulse wave sensor, an acceleration sensor 421, or a body movement sensor that detects body movement due to breathing using a piezoelectric element or the like. The respiratory cycle sensor 603 transmits a signal corresponding to the respiratory cycle of the subject to the information processing device 1. The information processing device 1 receives the signal transmitted from the respiratory cycle sensor 603.

[0104] 19 is a flowchart showing an information processing procedure according to embodiment 6. The control unit 11 of the information processing device 1 displays a monitor screen in the same procedure as steps S111 to S113 of embodiment 1 (step S611), generates an echo image (step S612), and displays the generated echo image on the display unit 15 (step S613).

[0105] Next, the control unit 11 receives the respiratory cycle signal transmitted from the respiratory cycle sensor 603 (step S614), and associates the information of the received respiratory cycle signal with the echo image generated at the same time (step S615).

[0106] Next, the control unit 11 selects multiple echo images generated at the same breathing timing (step S616). For example, the control unit 11 selects multiple echo information associated with information on cycle signals of the inhalation timing. Alternatively, the control unit 11 may select multiple echo images associated with information on breathing cycle signals of the same inhalation timing.

[0107] Then, the control unit 11 calculates the image appropriateness by inputting the selected echo images at the same breathing timing into the appropriateness learning model 17 (step S617). After completing the process of step S617, the control unit 11 executes the processes of steps S115 to S122 of the first embodiment.

[0108] As described above, the ultrasound diagnostic apparatus according to the sixth embodiment is configured to input echo images generated at the same respiratory cycle timing into the appropriateness learning model 17, thereby enabling more accurate determination of the appropriateness of the echo images.

[0109] When the appropriateness learning model 17 is trained by machine learning using only the echo images at the expiratory timing, it is preferable to select the echo images at the expiratory timing and input them to the appropriateness learning model 17. Similarly, when the appropriateness learning model 17 is trained by machine learning using only the echo images at the inhalation timing, it is preferable to select the echo images at the inhalation timing and input them to the appropriateness learning model 17. Furthermore, a first appropriateness learning model trained by machine learning using the echo images at the inhalation timing and a second appropriateness learning model trained by machine learning using the echo images at the inhalation timing may be provided, and an echo image generated at the expiratory timing may be input to the first appropriateness learning model, and an echo image generated at the inhalation timing may be input to the second appropriateness learning model.

[0110] (Embodiment 7) The ultrasound diagnostic device according to embodiment 7 differs from embodiment 1 in that it collects echo images obtained using a plurality of ultrasound frequencies. Since the other configurations of the information processing device 1 are the same as those of the information processing device 1 according to embodiment 1, the same reference numerals are used for the same parts and detailed description thereof will be omitted.

[0111] FIG. 20 is a block diagram showing an example of the configuration of an appropriateness learning model 17 according to the seventh embodiment. The information processing device 1 periodically switches between a drive signal for transmitting ultrasound waves of a first frequency and a drive signal for transmitting ultrasound waves of a second frequency, outputs the switched drive signal to the ultrasound probe 2, and receives echo signals. The switching period is short enough to allow the same scanning site to be scanned with the first frequency and the second frequency. By using ultrasound waves of different frequencies, different echo images can be obtained for the same scanning site. Generally, the higher the frequency of ultrasound, the higher the resolution but the lower the penetration power, and the lower the frequency, the lower the resolution and the higher the penetration power.

[0112] The control unit 11 of the information processing device 1 calculates the image appropriateness by inputting multiple echo images obtained with ultrasound of a first frequency and multiple echo images obtained with ultrasound of a second frequency into the appropriateness learning model 17.

[0113] By inputting echo images obtained by using ultrasound of different frequencies for the same scanning area into the appropriateness learning model 17, more detailed information about the echo image can be obtained, and the image appropriateness can be calculated to more accurately indicate whether the image is appropriate for diagnosing pulmonary disease.

[0114] As described above, according to the ultrasound diagnostic device of the seventh embodiment, by inputting multiple echo images obtained using ultrasound of different frequencies into the appropriateness learning model 17, it is possible to more accurately determine whether or not the echo images are suitable for diagnosing a specific lung disease, and collect them. [Explanation of symbols]

[0115] 1. Information processing equipment 2 Ultrasound probes 10 Recording media 11 Control section 11a Probe control section 11b Image generation section 11c Pulmonary diagnostic adequacy assessment section 11d Pulmonary diagnostic image storage unit 11e Lung diagnostic image collection display processing unit 11f Pulmonary congestion calculation section 11g Pulmonary congestion display processing unit 12 Memory 13 Storage section 14 Control section 15 Display 16 Communications Department 17 Relevance Learning Model 31 A-Line 32 Bat Sign 33 B Line 34 Ground-glass opacity 131 Computer Programs 151 Echo image display unit 152 Collection Gauge 152a Meter Block 153 Pulmonary diagnostic image display unit 154 Congestion level display unit 155 Start button 156 Stop button 171 Individualized Learning Model 172 Integrated Learning Model 218 Indicator Learning Model 603 Breathing Cycle Sensor 421 Acceleration Sensor

Claims

1. generating a series of images based on signals obtained from a scanning probe scanning an organ of the subject; selecting the plurality of images generated at the same respiratory timing based on a respiratory cycle signal output from a respiratory cycle sensor that detects the respiratory cycle of the subject; determining whether the selected images at the same respiratory timing are suitable for diagnosing a predetermined disease; storing the plurality of images at the same breathing timing determined to be suitable for diagnosing the predetermined disease as a plurality of suitable images; outputting a collection of the plurality of suitable images; A computer program that causes a computer to execute a process.

2. The process of generating the plurality of images includes: sequentially generating the plurality of images in real time based on signals obtained from the scanning probe; The process of outputting the collected amount includes: and displaying the collected volume and a predetermined target collected volume in real time in parallel with the process of generating the plurality of images based on signals obtained from the scanning probe.

2. The computer program of claim 1.

3. determining whether the plurality of suitable images has been collected at least as large as the target collection amount; calculating an index for diagnosing the predetermined disease based on the plurality of appropriate images having a collection amount equal to or greater than the target collection amount suitable for diagnosing the predetermined disease; View calculated metrics 3. A computer program product according to claim 2, for causing the computer to execute a process.

4. calculating an index for diagnosing the predetermined disease based on the plurality of appropriate images suitable for diagnosing the predetermined disease; Output the calculated indicators 3. A computer program according to claim 1 or 2, for causing a computer to execute a process.

5. the scanning probe is an ultrasound probe; The process of generating the plurality of images includes: generating a series of the plurality of images based on signals obtained from the scanning probe scanning the lungs of the subject; The process of calculating the index includes: Detecting B-lines in at least the plurality of suitable images; Calculating the index related to pulmonary congestion based on the detected B-lines 5. A computer program according to claim 3 or claim 4.

6. the scanning probe is an ultrasound probe; The process of generating the plurality of images includes: generating a series of the plurality of images based on signals obtained from the scanning probe scanning the lungs of the subject; The process of calculating the index includes: Detecting B-lines, ground-glass opacities, and bat signs based on ultrasound reflections from ribs in the plurality of suitable images; The index relating to pulmonary congestion is calculated based on the presence or absence or number of the B-lines, the presence or absence of the ground-glass opacity, and the contrast of the Bat sign.

5. A computer program according to claim 3 or claim 4.

7. the scanning probe is an ultrasound probe; The process of generating the plurality of images includes: generating a series of the plurality of images based on signals obtained from the scanning probe scanning the lungs of the subject; The process of calculating the index includes: and a process of calculating the index related to pulmonary congestion by inputting the plurality of appropriate images into an index learning model that outputs the index related to pulmonary congestion when the plurality of appropriate images are input.

5. A computer program according to claim 3 or claim 4.

8. The predetermined disease is pulmonary congestion, determining whether the plurality of suitable images has been collected at least as large as the target collection amount; detecting B-lines in the plurality of suitable images that are equal to or greater than the target acquisition volume suitable for diagnosing pulmonary congestion; determining that pulmonary congestion exists when a predetermined ratio or more of the images in which the B-line is detected are present among the plurality of appropriate images; If pulmonary congestion is determined, the proportion of B-lines present in the plurality of appropriate images is displayed.

3. A computer program product according to claim 2, for causing the computer to execute a process.

9. Displaying a representative image suitable for diagnosing the predetermined disease from among the plurality of appropriate images. The computer program according to any one of claims 1 to 8, for causing the computer to execute processing.

10. a plurality of individual learning models that extract and output feature values ​​of an input image when a single image is input, and extract feature values ​​of each of the plurality of images by inputting each of the selected images into the plurality of individual learning models; When the feature amounts of the plurality of images are input, the feature amounts output from the plurality of individual learning models are input to an integrated learning model that outputs an image appropriateness indicating the degree to which the plurality of images are appropriate for diagnosing the predetermined disease, thereby determining whether the plurality of images are appropriate images for diagnosing the predetermined disease. The computer program according to any one of claims 1 to 9.

11. estimating a scanning region of the subject based on a signal output from a positioning sensor provided on the scanning probe; outputting movement instruction information for instructing movement of the scanning probe so that a predetermined number or more of the plurality of images are generated at each of the plurality of scanning regions of the subject; The computer program according to any one of claims 1 to 10, for causing the computer to execute processing.

12. estimating the orientation of the scanning probe based on a signal output from an orientation sensor provided on the scanning probe; storing posture information indicating a posture of the scanning probe when the plurality of appropriate images suitable for diagnosing the predetermined disease are generated; When the number of images suitable for diagnosing the predetermined disease decreases, the apparatus outputs posture change instruction information for instructing a posture change of the scanning probe based on the stored posture information. The computer program according to any one of claims 1 to 11.

13. the scanning probe is an ultrasound probe that scans an organ of the subject by periodically switching between ultrasound waves of a first frequency and ultrasound waves of a second frequency; The process of generating the plurality of images includes: A series of images is generated based on signals obtained from the scanning probe, which scans the subject's organ while switching between ultrasonic frequencies. The computer program according to any one of claims 1 to 12.

14. generating a series of images based on signals obtained from a scanning probe scanning an organ of the subject; selecting the plurality of images generated at the same respiratory timing based on a respiratory cycle signal output from a respiratory cycle sensor that detects the respiratory cycle of the subject; determining whether the selected images at the same respiratory timing are suitable for diagnosing a predetermined disease; storing the plurality of images at the same breathing timing determined to be suitable for diagnosing the predetermined disease as a plurality of suitable images; and outputting a collection of the plurality of appropriate images suitable for diagnosing the predetermined disease. Information processing methods.

15. a generator for generating a series of images based on signals obtained from a scanning probe that scans an organ of a subject; a determination unit that selects, from the plurality of images generated by the generation unit, the plurality of images generated at the same respiratory timing based on a respiratory cycle signal output from a respiratory cycle sensor that detects the respiratory cycle of the subject, and determines whether the selected plurality of images at the same respiratory timing are images suitable for diagnosing a predetermined disease; a storage unit that stores the plurality of images at the same breathing timing that are determined by the determination unit to be suitable for diagnosing the predetermined disease as a plurality of appropriate images; an output unit that outputs a collection of the plurality of appropriate images suitable for diagnosing the predetermined disease; An information processing device comprising:

Images