Computer program, image output method, and image output device

Abstract

Provided are a computer program, an image output method, and an image output device.　This computer program causes a computer to execute processing including: acquiring information on a peritoneal dialysis patient including at least one of edema, urine volume, and fluid removal volume; generating an image representing the body fluid state of the peritoneal dialysis patient on the basis of the acquired information; and outputting the generated image.

Description

Computer program, image output method, and image output device

[0001] The present disclosure relates to a computer program, an image output method, and an image output device.

[0002] Various techniques have been proposed to detect the condition of a patient undergoing dialysis treatment. For example, patent documents disclose a technique in which the period of dialysis treatment is divided into multiple periods and the patient's blood pressure is measured during each period.

[0003] Japanese Patent Application Laid-Open No. 2000-000217

[0004] In Patent Document 1, the change in blood pressure during the dialysis period is merely managed, and neither the peritoneal dialysis patient nor the medical staff can intuitively grasp the patient's own bodily fluid status.

[0005] In one aspect, an object of the present invention is to provide a computer program, an image output method, and an image output device that enable intuitive understanding of the body fluid status of a peritoneal dialysis patient.

[0006] (1) The computer program disclosed herein is a computer program for causing a computer to execute a process of acquiring information about a peritoneal dialysis patient, including at least one of edema, urine volume, and amount of water removed, generating an image representing the body fluid status of the peritoneal dialysis patient based on the acquired information, and outputting the generated image.

[0007] (2) In the computer program of (1) above, it is preferable that the information further includes information on at least one of hANP (human atrial natriuretic peptide), cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion.

[0008] (3) In the computer program of (1) or (2) above, it is preferable that the information further includes information on at least one of the weight, dry mouth, antihypertensive drug, diuretic drug, and blood pressure of the peritoneal dialysis patient.

[0009] (4) In any one of the computer programs (1) to (3) above, it is preferable that the information further includes information on at least one of urinary cortisol, urinary creatinine, skin sodium, and Na-MRI (nuclear magnetic resonance).

[0010] (5) In any one of the computer programs (1) to (4) above, it is preferable that the image generated is an image that schematically shows the sodium storage and sodium excretion amounts in the peritoneal dialysis patient.

[0011] (6) In any one of the computer programs (1) to (5) above, it is preferable that the image includes a first container containing interstitial fluid and a second container containing plasma, and an image is generated that represents the amount of sodium stored based on the amount of interstitial fluid in the first container.

[0012] (7) In any one of the computer programs (1) to (6) above, the image includes a flow path through which the interstitial fluid and the plasma flow between the first container and the second container, and an excretory pathway through which sodium is excreted as urine from the second container, and an image is generated that schematically represents the amount of sodium excreted based on the amount of sodium removed from the plasma by peritoneal dialysis and the amount of sodium excreted through the excretory pathway.

[0013] (8) In any one of the computer programs (1) to (7) above, it is preferable that the image generated is an image that schematically represents the balance between the sodium intake and sodium excretion of the peritoneal dialysis patient.

[0014] (9) In any one of the computer programs (1) to (8) above, it is preferable to input the acquired information of the peritoneal dialysis patient into a learning model that has been trained to output information regarding the body fluid status when information regarding at least one of edema, urine volume, and amount of water removed is input, perform calculations using the learning model, and generate an image representing the body fluid status of the peritoneal dialysis patient based on the calculation results using the learning model.

[0015] (10) The image output method disclosed herein acquires information about a peritoneal dialysis patient, including at least one of edema, urine volume, and amount of water removed, generates an image representing the fluid status of the peritoneal dialysis patient based on the acquired information, and outputs the generated image, which is performed by a computer.

[0016] (11) The image output device of the present disclosure includes at least one processor, which acquires information about a peritoneal dialysis patient, including at least one of edema, urine volume, and amount of water removed, generates an image representing the fluid status of the peritoneal dialysis patient based on the acquired information, and outputs the generated image.

[0017] In one aspect, the fluid status of a peritoneal dialysis patient can be intuitively grasped.

[0018] FIG. 1 is a schematic diagram showing an example of the configuration of a body fluid management support system according to an embodiment. FIG. 2 is a block diagram illustrating the internal configuration of a server device. FIG. 3 is a block diagram illustrating the internal configuration of a patient terminal. FIG. 4 is a block diagram illustrating the internal configuration of a doctor terminal. FIG. 5 is a schematic diagram illustrating an example of a patient information input screen on a patient terminal. FIG. 6 is a schematic diagram illustrating an example of a patient information input screen on a doctor terminal. FIG. 7 is a schematic diagram illustrating an example of the configuration of a learning model. FIG. 8 is a schematic diagram illustrating an example of image generation. FIG. 9 is a schematic diagram illustrating an example of image generation. FIG. 10 is a schematic diagram illustrating a modified example of an image generated in the server device. FIG. 11 is a schematic diagram illustrating a modified example of an image generated in the server device. FIG. 12 is a flowchart illustrating the procedure of processing executed by the server device according to the present embodiment.

[0019] The present invention will be described in detail below with reference to the drawings illustrating embodiments. (Embodiment 1) Fig. 1 is a schematic diagram illustrating an example of the configuration of a body fluid management support system according to an embodiment. The body fluid management support system according to the embodiment includes a server device 10, a patient terminal 20, and a doctor terminal 30. The server device 10 is a server computer installed, for example, by a hospital. The patient terminal 20 is a client computer used by a peritoneal dialysis patient (hereinafter simply referred to as a patient), and the doctor terminal 30 is a client computer used by a medical professional such as a doctor. The server device 10, the patient terminal 20, and the doctor terminal 30 are connected to each other so as to be able to communicate with each other via a communication network NW.

[0020] The server device 10 acquires information about the peritoneal dialysis patient (hereinafter referred to as patient information) from the patient terminal 20 and the doctor terminal 30, and generates an image showing the body fluid status of the peritoneal dialysis patient based on the acquired patient information.

[0021] The patient information acquired by the server device 10 includes information on at least one of edema, urine volume, and amount of water removed. The information on edema, urine volume, and amount of water removed can be received through the patient terminal 20. The patient information acquired by the server device 10 may further include information such as dry mouth and weight input from the patient terminal 20.

[0022] The patient information acquired by the server device 10 may further include information on at least one of hANP, cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion. This information is managed for each patient and may be input from the doctor's terminal 30 and pre-registered in the server device 10, for example. The patient information registered in the server device 10 may further include information on antihypertensive drugs, diuretics, blood pressure, etc., and may further include information on urinary cortisol, urinary creatinine, skin sodium, Na-MRI, etc.

[0023] The server device 10 outputs an image generated based on patient information. For example, the server device 10 may transmit the generated image to a patient terminal 20 via the communication network NW to present the patient with the body fluid status. The patient can intuitively understand their own body fluid status. Alternatively, the server device 10 may transmit the generated image to a doctor terminal 30 via the communication network NW to present the patient's body fluid status to a medical professional. Medical professionals can use the image as a community tool to understand the patient's body fluid status and provide guidance on appropriate body fluid management.

[0024] The internal configuration of each device that constitutes the body fluid management support system will be described below.

[0025] 2 is a block diagram illustrating the internal configuration of the server device 10. The server device 10 is a dedicated or general-purpose computer, and includes a control unit 11, a storage unit 12, a communication unit 13, an operation unit 14, and a display unit 15.

[0026] The control unit 11 includes, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), and a RAM (Random Access Memory). The ROM included in the control unit 11 stores a control program that controls the operation of each hardware unit included in the server device 10. The CPU in the control unit 11 reads and executes the control program stored in the ROM and a computer program (described below) stored in the storage unit 12, and controls the operation of each hardware unit, thereby causing the entire device to function as the server device 10 (image output device) of the present disclosure. The RAM included in the control unit 11 temporarily stores data used during calculation and control.

[0027] In the embodiment, the control unit 11 is configured to include a CPU, a ROM, and a RAM, but the configuration of the control unit 11 is not limited to the above. The control unit 11 may be, for example, one or more control circuits or arithmetic circuits including a GPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor), a quantum processor, volatile or non-volatile memory, etc. The control unit 11 may also have functions such as a clock that outputs date and time information, a timer that measures the elapsed time from when a measurement start instruction is given until when a measurement end instruction is given, and a counter that counts numbers.

[0028] The storage unit 12 includes a storage device such as a hard disk drive (HDD), a solid state drive (SSD), etc. The storage unit 12 stores various computer programs executed by the control unit 11 and various data used by the control unit 11.

[0029] The computer program (program product) stored in the storage unit 12 includes an image generation program PG for causing a computer to execute a process of acquiring information about a peritoneal dialysis patient, including at least one of edema, urine volume, and amount of water removed, generating an image representing the body fluid status of the peritoneal dialysis patient based on the acquired information, and outputting the generated image. The image generation program PG may be a single computer program or a group of programs consisting of multiple computer programs. The image generation program PG may also partially use an existing library. The image generation program PG may be executed by a single computer or by multiple computers working together.

[0030] A computer program including the image generating program PG is provided by a non-transitory recording medium RM on which the computer program is readably recorded. The recording medium RM is a portable memory such as a CD-ROM, a USB memory, or an SD (Secure Digital) card. The control unit 11 reads various computer programs from the recording medium RM using a reading device (not shown) and stores the read various computer programs in the memory unit 12. The computer program including the image generating program PG may be provided via communication. The control unit 11 acquires the computer program including the image generating program PG by communication via the communication unit 13 and stores the acquired computer program in the memory unit 12.

[0031] The memory unit 12 may also store a learning model LM that has been trained to output information about the body fluid status when information about at least one of edema, urine volume, and water removal volume is input. The memory unit 12 stores information describing the learning model LM, such as the layer structure of the model, the number and connection relationships of nodes that make up each layer, and weight coefficients and biases between nodes, as well as learned parameters, for the trained learning model LM.

[0032] The communication unit 13 includes a communication interface for transmitting and receiving various data to and from an external device. The communication interface of the communication unit 13 may be a communication interface conforming to a communication standard such as Wi-Fi (registered trademark), LAN (Local Area Network), Bluetooth (registered trademark), ZigBee (registered trademark), 3G, 4G, 5G, or LTE (Long Term Evolution). When data to be transmitted is input from the control unit 11, the communication unit 13 transmits the data to the destination external device. When data transmitted from the external device is received, the communication unit 13 outputs the received data to the control unit 11. In this embodiment, examples of external devices are the patient terminal 20 and the doctor terminal 30.

[0033] The operation unit 14 includes operation devices such as a touch panel, a keyboard, and switches, and receives various operations and settings from a user, etc. The control unit 11 performs appropriate control based on various pieces of operation information provided by the operation unit 14, and stores setting information in the storage unit 12 as necessary.

[0034] The display unit 15 includes a display device such as a liquid crystal display or an organic EL (Electro-Luminescence) display, and displays information to be presented in response to an instruction from the control unit 11 .

[0035] In this embodiment, the server device 10 may be a single computer, or may be a computer system configured with multiple computers and peripheral devices, etc. Furthermore, the server device 10 may be a virtual machine whose entity is virtualized, or may be a cloud.

[0036] 3 is a block diagram illustrating the internal configuration of the patient terminal 20. The patient terminal 20 is a computer such as a smartphone, a tablet terminal, or a personal computer, and includes a control unit 21, a storage unit 22, a communication unit 23, an operation unit 24, and a display unit 25.

[0037] The control unit 21 includes, for example, a CPU, a ROM, a RAM, etc. The CPU of the control unit 21 controls the operation of the various hardware components described above by loading various programs stored in advance in the ROM or the storage unit 22 into the RAM and executing them, thereby causing the entire device to function as the patient terminal 20 of the present disclosure.

[0038] The control unit 21 is not limited to the above configuration, and may be configured as a single piece of hardware (SoC: System On a Chip) that integrates a processor, memory, storage, communication interface, etc. The control unit 21 may also have functions such as a clock that outputs date and time information, a timer that measures the elapsed time from when an instruction to start measurement is given until when an instruction to end measurement is given, and a counter that counts numbers.

[0039] The storage unit 22 includes storage such as a memory and a hard disk. The storage unit 22 stores various computer programs executed by the control unit 21, data necessary for executing the computer programs, etc. The computer programs stored in the storage unit 22 include application programs for using the services provided by the server device 10.

[0040] The programs stored in the storage unit 22 may be provided by a non-transitory storage medium on which the programs are readably recorded. The storage medium is, for example, a portable memory such as a CD-ROM, a USB memory, or an SD card. The control unit 21 reads the various programs from the storage medium using a reading device (not shown) and installs the read programs in the storage unit 22. The programs stored in the storage unit 22 may also be provided by communication. In this case, the control unit 21 acquires the various programs through the communication unit 23 and installs the acquired programs in the storage unit 22.

[0041] The communication unit 23 includes a communication interface for connecting to the communication network NW. The communication interface included in the communication unit 23 is a communication interface conforming to a communication standard such as Wi-Fi (registered trademark), LAN, Bluetooth (registered trademark), ZigBee (registered trademark), 3G, 4G, 5G, LTE, etc. The communication unit 23 transmits various information to be notified to the outside and receives various information transmitted from the outside to the device itself.

[0042] The operation unit 24 is equipped with input devices such as a touch panel and operation buttons, and receives various types of operation information and setting information. The operation unit 24 outputs the received operation information and setting information to the control unit 21. The control unit 21 performs appropriate control based on the operation information input from the operation unit 24, and stores the setting information in the storage unit 22 as necessary.

[0043] The display unit 25 includes a display device such as a liquid crystal display or an organic EL display, and displays information to be presented to the patient based on a control signal output from the control unit 21 .

[0044] 4 is a block diagram illustrating the internal configuration of the doctor terminal 30. The doctor terminal 30 is a computer such as a personal computer, a tablet terminal, or a smartphone, and includes a control unit 31, a storage unit 32, a communication unit 33, an operation unit 34, and a display unit 35.

[0045] The control unit 31 includes, for example, a CPU, a ROM, a RAM, etc. The CPU of the control unit 31 controls the operation of the various hardware components described above by loading various programs stored in advance in the ROM or the storage unit 32 into the RAM and executing them, thereby causing the entire device to function as the doctor terminal 30 of the present disclosure.

[0046] The control unit 31 is not limited to the above configuration, and may be configured as a single piece of hardware (SoC: System On a Chip) that integrates a processor, memory, storage, communication interface, etc. The control unit 31 may also have functions such as a clock that outputs date and time information, a timer that measures the elapsed time from when an instruction to start measurement is given until when an instruction to end measurement is given, and a counter that counts numbers.

[0047] The storage unit 32 includes storage such as a memory and a hard disk. The storage unit 32 stores various computer programs executed by the control unit 31, data necessary for executing the computer programs, etc. The computer programs stored in the storage unit 32 include application programs for using the services provided by the server device 10.

[0048] The programs stored in the storage unit 32 may be provided by a non-transitory storage medium on which the programs are readably recorded. The storage medium is, for example, a portable memory such as a CD-ROM, a USB memory, or an SD card. The control unit 31 reads the various programs from the storage medium using a reading device (not shown) and installs the read programs in the storage unit 32. The programs stored in the storage unit 32 may also be provided by communication. In this case, the control unit 31 acquires the various programs through the communication unit 33 and installs the acquired programs in the storage unit 32.

[0049] The communication unit 33 includes a communication interface for connecting to the communication network NW. The communication interface included in the communication unit 33 is a communication interface conforming to a communication standard such as Wi-Fi (registered trademark), LAN, Bluetooth (registered trademark), ZigBee (registered trademark), 3G, 4G, 5G, LTE, etc. The communication unit 33 transmits various information to be notified to the outside and receives various information transmitted from the outside to the device itself.

[0050] The operation unit 34 is equipped with input devices such as a keyboard and a mouse, and receives various types of operation information and setting information. The operation unit 34 outputs the received operation information and setting information to the control unit 31. The control unit 31 performs appropriate control based on the operation information input from the operation unit 34, and stores the setting information in the storage unit 32 as necessary.

[0051] The display unit 35 includes a display device such as a liquid crystal display or an organic EL display, and displays information to be presented to medical personnel based on a control signal output from the control unit 31 .

[0052] The operation of the body fluid management support system will be described below. When using the body fluid management support system according to this embodiment to display the patient's own body fluid status on the display unit 25 of the patient terminal 20, the patient inputs their own patient information via the operation unit 24 of the patient terminal 20.

[0053] Fig. 5 is a schematic diagram showing an example of a patient information input screen on the patient terminal 20. The patient information input screen shown in Fig. 5 shows an example of a screen that is displayed on the display unit 25 when the server device 10 is accessed from the patient terminal 20 and a predetermined operation is performed. This patient information input screen accepts information regarding edema, urine volume, and amount of water removed.

[0054] The state of edema is measured by the patient themselves by checking the indentation. Specifically, the patient applies pressure to the area where the bone, such as the tibia, is subcutaneously located with the thumb or the second through fourth fingers, and then visually and palpably examines the skin after releasing the pressure. An edema level of "0" indicates a normal state with no indentation. An edema level of "1+" indicates a slight indentation, but no visible distortion, and the indentation disappears rapidly. An edema level of "2+" indicates a moderately deep indentation that disappears within 10 to 15 seconds. An edema level of "3+" indicates a deep indentation that may last for more than one minute. An edema level of "4+" indicates a very deep indentation that may last for more than two to five minutes. The patient information input screen in Figure 5 allows the patient to input the state of edema, categorizing it into three categories: "0-1+," "2+," and "3+-4+." Alternatively, the state of edema may be input by classifying it into five states from "0" to "4+".

[0055] The amount of urine is measured by the patient themselves using a measuring cup or the like. The amount of urine (mL) per day is entered on the patient information input screen in Figure 5. The amount of fluid removed is calculated as the infusion amount minus the drainage amount. Here, the infusion amount represents the amount of dialysis fluid injected into the patient's body and is measured by weighing the bag containing the dialysis fluid before and after injection. The drainage amount represents the amount of dialysis fluid discharged from the patient's body and is measured by weighing the bag containing the drainage fluid. The amount of fluid removed (mL) per day is entered on the patient information input screen in Figure 5.

[0056] Figure 5 illustrates an example of a patient information input screen that accepts information regarding edema, urine volume, and water removal volume, but it may also be configured to accept any one or two of edema, urine volume, and water removal volume.

[0057] Furthermore, the patient information input screen may accept information on dry mouth or weight. Dry mouth is subjectively rated by the patient using an NRS (numerical rating scale) of 0 to 10. Alternatively, dry mouth may be rated on a two-level scale: dry mouth / no dry mouth. Weight is measured by the patient using a weighing scale.

[0058] Furthermore, some of the patient information may be entered by a medical professional. Fig. 6 is a schematic diagram showing an example of a patient information input screen on the doctor terminal 30. The patient information input screen shown in Fig. 6 is an example of a screen displayed on the display unit 35 when the doctor terminal 30 accesses the server device 10 and performs a predetermined operation. This patient information input screen accepts information regarding hANP, cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion.

[0059] hANP is a human atrial natriuretic peptide, and its concentration is measured by a blood test. The IRMA (immunoradiometric assay) method is used for concentration measurement. The reference value for hANP is, for example, 43.0 pg / mL. Because hAMP is secreted in response to atrial expansion stimulation, it exhibits abnormally high values ​​in diseases or pathological conditions that cause increased atrial pressure or increased body fluid volume. In the example screen shown in Figure 6, the hAMP concentration is entered as a numerical value (pg / mL).

[0060] The cardiothoracic ratio is an index that represents the ratio of the cardiac transverse diameter to the thoracic diameter, and is measured using a chest X-ray. A ratio of 50% or less is considered appropriate for men and 55% or less for women. When the extracellular fluid volume increases, the amount of water in the blood vessels also increases, causing the heart to become larger, resulting in an increase in the cardiothoracic ratio. In the example screen shown in Figure 6, the cardiothoracic ratio is entered as a numerical value (%).

[0061] Bioimpedance is the resistance (impedance) of a living body and is measured by a body composition analyzer. Body composition analyzers use a bioelectrical impedance analysis (BIA) method that takes advantage of the fact that almost no current flows through fat tissue, but that current flows easily through lean tissue containing water and electrolytes. By using this BIA method, a body composition analyzer can measure an indicator of body fluid surplus / deficiency (OH). The reference value for OH is -1.1 to 1.1 L. In the example screen shown in Figure 6, the indicator of body fluid surplus / deficiency is input as a numerical value (L).

[0062] The transperitoneal sodium removal amount is measured, for example, by collecting the dialysate (effluent) discharged from the patient's body during peritoneal dialysis and analyzing the components of the collected effluent. In the example screen of Figure 6, the transperitoneal sodium removal amount is input as a value (g) converted to salt.

[0063] The amount of urinary sodium removal is measured, for example, by collecting urine from a patient and analyzing the components of the collected urine. In the example screen of Figure 6, the amount of urinary sodium removal is input as a value (g) converted to salt.

[0064] 6 illustrates an example of a patient information input screen that accepts information on hANP, cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion, but the screen may accept any one or more of these pieces of information. Furthermore, it is not necessary to accept all of the information on a single input screen, and each piece of information may be accepted individually.

[0065] Furthermore, the patient information input screen may accept information regarding at least one of antihypertensive drugs, diuretics, blood pressure, urinary cortisol, urinary creatinine, skin sodium, and Na-MRI.

[0066] The patient information input to the patient terminal 20 and the doctor terminal 30 is transmitted to the server device 10 via the communication network NW. The server device 10 generates an image representing the patient's bodily fluid status based on the patient information acquired from the patient terminal 20 and the doctor terminal 30. In this embodiment, the learning model LM is used to derive information relating to the bodily fluid status from the patient information, and an image representing the patient's bodily fluid status is generated based on the derived information.

[0067] 7 is a schematic diagram showing an example of the configuration of the learning model LM. When patient information is input, the learning model LM is trained to output information about the patient's body fluid status. Known learning models such as CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), LSTM (Long Short Term Memory), and autoencoder are used for the learning model LM.

[0068] The learning model LM comprises an input layer, an intermediate layer, and an output layer. Each layer comprises one or more nodes. Patient information is input to the nodes of the input layer. Information on at least one of edema, urine volume, and fluid removal volume is input to the nodes of the input layer. The information input to the nodes of the input layer may further include information on at least one of hANP, cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion. Furthermore, the information input to the nodes of the input layer may include information on at least one of the patient's weight, dry mouth, antihypertensive medication, diuretics, and blood pressure. Furthermore, the information input to the nodes of the input layer may include information on at least one of urinary cortisol, urinary creatinine, skin sodium, and Na-MRI.

[0069] The intermediate layer of the learning model LM is composed of one or more layers. Each node in the intermediate layer is connected to nodes in the preceding and succeeding layers. The activity level of each node and the strength of the connections between nodes (weighting coefficients) are determined during the learning process. Each node in the layer outputs a value calculated based on the weighting coefficient and activity level to the next node. The output layer calculates probabilities using a softmax function based on values ​​input from the intermediate layer and outputs information based on the calculated probabilities. For example, the output layer calculates the probability that body fluid volume is normal, the probability that there is a tendency for excess, and the probability that there is excess, and outputs information based on the probabilities. For example, the learning model LM may output information determining whether a patient's body fluid volume is normal, tends to be excessive, or is excessive, based on input patient information. Such a learning model LM is generated by training using an existing learning algorithm using a dataset containing patient information such as edema, urine volume, and amount of water removed obtained from the patient, and actual measurements (correct values) of body fluid volume measured using a measuring device such as a body composition analyzer.

[0070] It is assumed that a trained learning model LM is stored in the memory unit 12 of the server device 10. When the control unit 11 of the server device 10 acquires patient information from the patient terminal 20 or the doctor terminal 30, it inputs the acquired patient information into the learning model LM, executes calculations, and acquires the calculation results from the learning model LM. In this embodiment, the calculation results from the learning model LM are information that determines whether the patient's body fluid volume is normal, tends to be excessive, or is excessive.

[0071] The control unit 11 of the server device 10 generates images representing the patient's bodily fluid status based on the calculation results of the learning model LM. FIGS. 8A-8C are schematic diagrams showing examples of generated images. Each image depicts a first container V1 containing interstitial fluid, a second container V2 containing plasma, a flow path P1 connecting the first container V1 and the second container, and an excretion path P2 through which fluid is discharged from the second container. The interstitial fluid contained in the first container V1 and the plasma contained in the second container V2 can flow from one container to the other through the flow path P1. The amount of sodium excreted as urine is depicted as the amount of fluid discharged from this excretion path P2. The amount of sodium removed by dialysis is depicted by the number of ">" symbols.

[0072] 8A shows that sodium removal by peritoneal dialysis is sufficient and that bodily fluid management is good. When the learning model LM determines that the patient's bodily fluid volume is normal, the control unit 11 generates and outputs an image such as that shown in FIG. 8A. The output destination may be the patient terminal 20 or the doctor terminal 30.

[0073] 8B shows that sodium removal by peritoneal dialysis is insufficient, and that fluid management is somewhat poor. When the learning model LM determines that the patient's fluid volume is excessive, the control unit 11 generates an image showing that the interstitial fluid volume and plasma volume are somewhat high, as shown in FIG. 8B, and outputs the image to the patient terminal 20 or the doctor terminal 30.

[0074] 8C shows that sodium removal by peritoneal dialysis is not working properly, indicating poor fluid management. When the learning model LM determines that the patient has an excessive amount of fluid, the control unit 11 generates an image of the first container V1 and the second container V2 filled with interstitial fluid and plasma, respectively, as shown in FIG. 8C, and outputs the image to the patient terminal 20 or the doctor terminal 30.

[0075] In the examples of Figures 8A-8C, images are shown that depict the patient's body fluid volume (interstitial fluid volume and plasma volume), as well as the amount of sodium excreted in urine and the amount of sodium removed by dialysis, but it is also possible to omit depicting the amount of sodium and generate an image that depicts only the body fluid volume.

[0076] 8A-8C from the server device 10, the patient terminal 20 displays the received image on the display unit 25. By checking the image displayed on the display unit 25, the patient can intuitively understand the state of his or her bodily fluids.

[0077] 8A to 8C from the server device 10, the doctor terminal 30 displays the received image on the display unit 35. By checking the image displayed on the display unit 35, medical professionals can understand the patient's bodily fluid status and use it as a community tool for providing guidance on appropriate bodily fluid management.

[0078] 9A and 9B are schematic diagrams showing modified examples of images generated by the server device 10. Figures 9A and 9B show a schematic representation of the balance between the amount of sodium ingested and the amount of sodium excreted. These figures reveal that even when the amount of sodium ingested and the amount of sodium excreted as urine by residual renal function (residual kidney) are approximately constant, the Na balance is disrupted when the amount of sodium removed by peritoneal dialysis (PD) changes.

[0079] 10 is a flowchart illustrating the procedure of processing executed by the server device 10 according to this embodiment. The server device 10 acquires patient information from the patient terminal 20 and the doctor terminal 30 via the communication network NW (step S101). The patient information acquired by the server device 10 includes information on at least one of edema, urine volume, and water removal volume. In addition to this information, information such as hANP, cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion may also be acquired.

[0080] The control unit 11 of the server device 10 inputs the acquired patient information into the learning model LM and executes calculations using the learning model LM (step S102). The control unit 11 acquires information about the patient's bodily fluid status as a result of calculations using the learning model LM (step S103).

[0081] The control unit 11 generates an image representing the patient's bodily fluid status based on the acquired information on the patient's bodily fluid status (step S104). The control unit 11 may generate images such as those shown in FIGS. 8A-8C, in which the interstitial fluid volume in the first container V1 and the plasma volume in the second container V2 are varied depending on whether the bodily fluid volume is normal, excessive, or excessive. The control unit 11 may also generate images, as shown in FIGS. 8A-8C, in which the amount of sodium removed by dialysis is varied depending on the interstitial fluid volume and plasma volume. Alternatively, the control unit 11 may generate images such as those shown in FIGS. 9A-9B, which schematically show the balance between the amount of sodium ingested and the amount of sodium excreted.

[0082] The control unit 11 transmits the generated image to the patient terminal 20 or the doctor terminal 30 (step S105). The image transmitted from the server device 10 reaches the patient terminal 20 or the doctor terminal 30 through the communication network NW. When the patient terminal 20 receives the image transmitted from the server device 10, it displays the received image on the display unit 25. When the doctor terminal 30 receives the image transmitted from the server device 10, it displays the received image on the display unit 35.

[0083] As described above, in this embodiment, the patient's bodily fluid status can be presented to the patient himself / herself and to medical professionals. The patient can intuitively understand his / her bodily fluid status by checking the image displayed on the display unit 25 of the patient terminal 20. Medical professionals can understand the patient's bodily fluid status by checking the image displayed on the display unit 35 of the doctor terminal 30, and can use it as a community tool to provide guidance on appropriate bodily fluid management.

[0084] The embodiments disclosed herein are illustrative in all respects and should not be considered limiting. The scope of the present invention is defined by the claims, not by the above meaning, and is intended to include all modifications within the meaning and scope of the claims.

[0085] For example, in the present embodiment, the server device 10 is configured to execute calculations using the learning model LM and generate an image using the calculation results of the learning model LM, but the patient terminal 20 and the doctor terminal 30 may each execute calculations using the learning model LM and generate an image using the calculation results of the learning model LM. In this case, the image generation program PG and the learning model LM are installed in the patient terminal 20 and the doctor terminal 30, respectively.

[0086] Furthermore, in this embodiment, patient information is input into the learning model LM to obtain information regarding the bodily fluid status, and an image representing the patient's bodily fluid status is generated based on the obtained information. However, it is also possible to use an image generation learning model such as GAN (Generative Adversarial Network) or PGGAN (Progressive Growing of GANs) to generate an image representing the patient's bodily fluid status directly from the patient information.

[0087] REFERENCE SIGNS LIST 10 Server device 11 Control unit 12 Memory unit 13 Communication unit 14 Operation unit 15 Display unit 20 Patient terminal 21 Control unit 22 Memory unit 23 Communication unit 24 Operation unit 25 Display unit 30 Doctor terminal 31 Control unit 32 Memory unit 33 Communication unit 34 Operation unit 35 Display unit PG Image generation program LM Learning model

Claims

1. A computer program for causing a computer to execute the following processes: acquiring information on a peritoneal dialysis patient, including at least one of edema, urine volume, and amount of water removed; generating an image showing the bodily fluid status of the peritoneal dialysis patient based on the acquired information; and outputting the generated image.

2. The computer program according to claim 1, wherein the information further includes information on at least one of human atrial natriuretic peptide (hANP), cardiothoracic ratio, bioimpedance, transperitoneal sodium removal, and urinary sodium excretion.

3. The computer program according to claim 2, wherein the information further includes information regarding at least one of the weight, dry mouth, antihypertensive drugs, diuretics, and blood pressure of the peritoneal dialysis patient.

4. The computer program product of claim 3, wherein the information further comprises information regarding at least one of urinary cortisol, urinary creatinine, skin sodium, and Na-MRI (nuclear magnetic resonance).

5. The computer program according to claim 1, for causing the computer to execute a process of generating, as the image, an image that diagrammatically represents sodium storage and sodium excretion in the peritoneal dialysis patient.

6. A computer program as claimed in claim 5, for causing the computer to execute a process of generating an image including a first container for accommodating interstitial fluid and a second container for accommodating plasma, the image representing the amount of sodium stored being determined by the amount of interstitial fluid in the first container.

7. A computer program as claimed in claim 6, for causing the computer to execute a process of generating an image including, as the image, a flow path through which the interstitial fluid and the plasma circulate between the first container and the second container, and an excretory pathway through which sodium is excreted as urine from the second container, the image being a schematic representation of the amount of sodium excreted based on the amount of sodium removed from the plasma by peritoneal dialysis and the amount of sodium excreted through the excretory pathway.

8. The computer program according to claim 1, for causing the computer to execute a process of generating, as the image, an image that diagrammatically represents the balance between sodium intake and sodium excretion of the peritoneal dialysis patient.

9. A computer program as claimed in any one of claims 1 to 8, for causing a computer to execute the following process: inputting the acquired information of the peritoneal dialysis patient into a learning model trained to output information regarding the bodily fluid status when information regarding at least one of edema, urinary volume, and amount of water removed is input, executing a calculation using the learning model, and generating an image representing the bodily fluid status of the peritoneal dialysis patient based on the calculation results using the learning model.

10. An image output method which comprises the steps of: acquiring information on a peritoneal dialysis patient, including at least one of edema, urine volume, and amount of water removed; generating an image showing the bodily fluid status of the peritoneal dialysis patient based on the acquired information; and outputting the generated image.

11. An image output device comprising at least one processor, which acquires information of a peritoneal dialysis patient including at least one of edema, urinary volume, and amount of water removed, generates an image showing the bodily fluid status of the peritoneal dialysis patient based on the acquired information, and outputs the generated image.

Images