Treatment optimization system and treatment optimization method

Abstract

This treatment optimization system comprises: a data collection unit that collects biological data of a treatment-target person; a data analysis unit that (1) causes an AI, which has been trained by machine learning, to analyze the biological data obtained before treatment and to create a detailed treatment plan suitable for the treatment-target person, (2) causes the AI to analyze biological data of the treatment-target person obtained during the treatment, to estimate, as an evaluation index of the treatment-target person with respect to the treatment, at least one of pain, numbness, a feeling of oppression, the presence or absence of a feeling of weakness, the presence or absence of a feeling of discomfort, a change in the feeling of discomfort, relief of the feeling of discomfort, and a feeling of comfort, and to create a detailed treatment plan corrected according to the estimated evaluation index of the treatment-target person, and (3) causes the AI to analyze biological data of the treatment-target person obtained after the treatment, to estimate the effect of the treatment, and to incorporate the obtained result in creating the next detailed treatment plan for the treatment-target person; and a display unit that presents, to a practitioner, the detailed treatment plan created by the AI.

Description

Treatment Optimization System and Treatment Optimization Method

[0001] The present invention relates to a treatment optimization system and a treatment optimization method. The present invention claims the priority of Japanese Patent Application No. 2024-206300 filed on November 27, 2024, and for designated countries where incorporation by reference is permitted, the contents described in that application are incorporated into the present application by reference.

[0002] For example, when a masseur or the like (hereinafter referred to as the practitioner) performs a treatment such as a massage on a person receiving the treatment (hereinafter referred to as the subject), the practitioner often performs the treatment based on the subjective evaluation of the subject regarding the treatment. Specifically, for example, during the treatment, the practitioner asks the subject questions such as "How is the strength? Isn't it too strong?" or "(Is this the right place for the painful area?)", and often adjusts the strength and position of the treatment according to the answers from the subject (for example, "It's a bit painful" or "A little lower").

[0003] However, the subject may refrain from answering the practitioner out of courtesy or think that enduring pain will have an effect, and may not answer honestly even if there is pain or discomfort. In such a case, the practitioner cannot obtain appropriate feedback from the subject, so trial and error such as adjusting the strength of the treatment or moving the treatment location cannot be done, and the optimal treatment for the subject cannot be performed.

[0004] Also, pain is not always caused by the area where the pain is felt, and is often pain such as referred pain. And when the cause of the pain is unknown, the treatment often becomes a palliative treatment. This is the same not only for treatments but also for medical treatments. Ideally, treatments and medical treatments should perform radical treatments rather than palliative treatments.

[0005] Regarding the adjustment of the treatment, for example, Patent Document 1 describes a massage machine that can obtain a treatment effect according to the delicate force adjustment desired by the subject.

[0006] Japanese Patent Application Laid-Open No. 2005-144058

[0007] Patent Document 1 describes an invention for a massage machine and cannot be applied to practitioners such as massage therapists. As mentioned above, conventional treatments performed by practitioners often rely on subjective evaluations by the person receiving the treatment, and there is a lack of a mechanism to quantitatively and objectively evaluate the effectiveness of the treatment. Furthermore, if the person receiving the treatment cannot accurately provide feedback on pain, etc., during or after the treatment, it becomes difficult to perform treatment that is appropriate for the person receiving the treatment.

[0008] This invention has been made in view of these circumstances, and aims to enable practitioners to perform treatments that are appropriate to the condition of the person receiving treatment.

[0009] This application includes several means to solve at least some of the above problems, and some examples are as follows.

[0010] To solve the above problems, a treatment optimization system according to one aspect of the present invention comprises: a data collection unit that collects the biological data of a person receiving treatment; a data analysis unit that causes an AI trained by machine learning to analyze the biological data before treatment and devise a treatment plan suitable for the person receiving treatment; the AI ​​to analyze the biological data of the person receiving treatment during treatment and estimate at least one of the following as evaluation indicators for the person receiving treatment: pain, numbness, pressure, weakness, presence or absence of discomfort, change in discomfort, resolution of discomfort, and pleasure; and devise a treatment plan modified according to the estimated evaluation indicators of the person receiving treatment; the AI ​​to analyze the biological data of the person receiving treatment after treatment to estimate the effect of the treatment and reflect this in devising the next treatment plan for the person receiving treatment; and a display unit that presents the treatment plan devised by the AI ​​to the practitioner.

[0011] The data analysis unit may also instruct the AI ​​to output the reasons for the plan, so as to achieve the objective of the practitioner explaining to the patient the purpose of the current treatment and the purpose of subsequent treatments, assuming continuous treatment.

[0012] The data analysis unit may also cause the AI ​​to output non-continuous alternatives to continuous treatment when the patient is not satisfied with the continuous treatment plan, and may also output the difference between the expected effect when the continuous treatment is performed on the patient and the expected effect when the alternative is performed on the patient.

[0013] The data analysis unit may input at least one of the following into the AI, along with the biometric data: the patient's attribute information, health status, medical history, and current symptoms, to have the AI ​​formulate a treatment plan suitable for the patient.

[0014] The data analysis unit may also cause the AI ​​to devise a treatment plan that promotes the secretion of hormones in the person receiving the treatment.

[0015] The data collection unit may collect, as biometric data, pre- and post-treatment physical data measurable using a wearable sensor, data representing changes in posture and movement measurable using a vision system, internal physiological data measurable using a physiological sensor, tactile data and pressure data measurable using a tactile sensor, and at least one of the subject's movements and facial expressions, including conversation and the content of conversation, measurable using an facial expression recognition system.

[0016] The treatment optimization system is characterized in that it shares the analysis results by the AI ​​with a specialist other than the practitioner and receives at least one of a professional diagnosis and treatment proposal from the specialist.

[0017] Another aspect of the present invention is a treatment optimization method using a treatment optimization system including a computer, comprising: a data collection step of collecting biological data of a person receiving treatment using the computer; a data analysis step of having an AI trained by machine learning analyze the biological data before treatment to plan a treatment plan suitable for the person receiving treatment; having the AI ​​analyze the biological data of the person receiving treatment during treatment to estimate at least one of the following as evaluation indicators for the person receiving treatment: pain, numbness, pressure, weakness, presence or absence of discomfort, change in discomfort, resolution of discomfort, and pleasure; and having the AI ​​plan a modified treatment plan according to the estimated evaluation indicators of the person receiving treatment; having the AI ​​analyze the biological data of the person receiving treatment after treatment to estimate the effect of the treatment and reflect this in planning the next treatment plan for the person receiving treatment; and a display step of presenting the treatment plan planned by the AI ​​to the practitioner.

[0018] According to the present invention, the practitioner can perform treatment that is appropriate to the condition of the person receiving treatment.

[0019] Other issues, configurations, and effects not mentioned above will be clarified by the following description of the embodiments.

[0020] Figure 1 shows an example of the configuration of a treatment optimization system according to one embodiment of the present invention. Figure 2 shows an example of the configuration of a terminal device. Figure 3 shows an example of the configuration of an analysis device. Figure 4 is a flowchart illustrating an example of treatment optimization processing by the treatment optimization system. Figure 5 shows an example of a proprietary examination. Figure 6 shows an example of a proprietary examination. Figure 7 shows an example of the skeletal state of a patient during a proprietary examination.

[0021] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In all drawings used to describe the embodiment, the same reference numerals will be used for identical components, and repeated descriptions will be omitted. In addition, in the following embodiment, the components (including element steps, etc.) are not necessarily essential unless specifically stated, or unless they are clearly essential in principle. Also, when referring to "consisting of A," "being made of A," "having A," or "including A," other elements are not excluded unless specifically stated to refer only to that element. Similarly, in the following embodiment, when referring to the shape, positional relationship, etc. of components, etc., it includes those that are substantially similar or approximate to their shape, etc., unless specifically stated, or unless it is clearly not the case in principle.

[0022] <Treatment Optimization System 10 According to One Embodiment of an Embodiment> Figure 1 shows an example of the configuration of the treatment optimization system 10 according to one embodiment of an embodiment of an embodiment.

[0023] The treatment optimization system 10, when practitioner 1 performs treatment on patient 2, plans a treatment plan suitable for patient 2 based on the patient 2's biometric data acquired before the treatment, suggests modifications to the treatment plan as needed based on the patient 2's biometric data acquired during the treatment, and evaluates the effectiveness of the treatment based on the patient 2's biometric data acquired after the treatment, and reflects this in the plan for the next treatment.

[0024] Here, "treatment" refers to therapies and procedures other than medical treatments. Specifically, this includes, for example, alternative therapies (therapies that deviate from traditional medical treatments (e.g., drug therapy and surgery), complementary therapies (therapies that complement medical treatments), integrative medicine (therapies that combine medical treatments and alternative therapies in an integrated manner), osteopathic therapy (therapies that adjust the balance and function of the body, especially manual therapy (massage, etc.)), and transcranial direct current stimulation, etc.

[0025] The treatment optimization system 10 comprises a group of sensors 20, a terminal device 30, and an analysis device 40.

[0026] The sensor group 20 includes at least one of the following for measuring the biological data of the person being treated 2: a wearable sensor 21, a vision system 22, a physiological sensor 23, a tactile sensor 24, and a facial expression recognition system 25. Hereinafter, when the wearable sensor 21, vision system 22, physiological sensor 23, tactile sensor 24, and facial expression recognition system 25 are not individually distinguished, they will be referred to as "each sensor." Each sensor measures the measurement items it can measure from the person being treated 2 according to a predetermined sampling period set for each sensor.

[0027] Here, the biometric data of the patient 2 includes at least one of the following: pre- and post-treatment physical data (such as movement level) measurable using wearable sensors (such as IMU (Inertial Measurement Unit), EMG (Surface Electromyography) sensors, and pressure sensors) 21; data representing changes in posture and movement measurable using a vision system (depth camera, computer vision) 22; internal physiological data measurable using physiological sensors (such as electrocardiogram sensors, heart rate sensors, oxygen saturation sensors, respiratory sensors, body temperature sensors, blood pressure sensors, blood glucose sensors, body composition sensors, electroencephalogram sensors, activity sensors, electromyogram sensors, and galvanic skin response sensors) 23; tactile data and pressure data measurable using tactile sensors 24; and facial expressions including the movements, conversations, and the content of conversations of the patient 2 (used to detect pain, discomfort, pleasure, stress, mania, depression, etc.) measurable using a facial recognition system 25. Note that the biometric data may also be data from tests measured externally and brought in by the patient (such as blood test data, MRI scan data, and X-ray images).

[0028] Furthermore, the system collects information such as the patient's gait upon arrival, responses during the consultation, conversation content, response speed and content to questions, and tone of voice, and analyzes the patient's physical and mental condition to address issues such as memory impairment, stress, bipolar disorder, and depression. While this data can be recorded, it is also possible to transmit this information about patient 2 to the analysis device 40, where the data analysis unit 41 of the analysis device 40 analyzes it using the AI ​​411. For example, new questions may be devised before treatment planning to gain a deeper understanding of patient 2's condition and address it accordingly. Specifically, for instance, if patient 2 is experiencing significant stress today, the system may plan to conduct counseling before treatment.

[0029] Furthermore, during the treatment, the content of the conversation between the practitioner 1 and the patient 2 during the treatment may be transmitted to the analysis device 40, and the data analysis unit 41 may have the AI ​​411 analyze it, and the analysis results may be reported to the practitioner 1 sequentially.

[0030] The patient receiving treatment (Patient 2) often has chronic symptoms, and it is said that many of these are caused by psychological factors such as stress. Therefore, in order to improve the psychological factors, it may be possible to design a treatment that can increase the secretion of so-called happiness hormones such as oxytocin, dopamine, and serotonin in Patient 2.

[0031] For example, oxytocin is said to be secreted by pressing the Hegu acupoint, as it activates the parasympathetic nervous system and balances the autonomic nervous system. Also, dopamine is said to be secreted by pressing the Hegu and Zusanli acupoints, as it relaxes the occipital bone, adjusts the balance in the brain, promotes the secretion of oxytocin in the pituitary gland, and relaxes the area around the heart, thereby activating receptors that sense oxytocin.

[0032] Incorporating these elements into the treatment can further enhance its effectiveness. Additionally, during conversations with the patient, suggestions for stress relief, such as encouraging physical contact or family meals, may be proposed. Furthermore, information analyzing the state of the mind, body, and brain, as well as new hormone secretion, adjustment treatments, and prescriptions, may be incorporated in the future.

[0033] Furthermore, the data analysis unit 41 may instruct the AI ​​411 to display and instruct information for the practitioner to explain and obtain consent from before the procedure, so as to prevent problems later on with procedures involving the delicate zone, and with commonly known healing reactions and side effects.

[0034] Furthermore, the data analysis unit 41 may provide more detailed explanations in response to requests from the practitioner or the person receiving treatment, and may also instruct the AI ​​411 to develop alternative plans if the person receiving treatment does not comply.

[0035] Furthermore, the data analysis unit 41 may instruct the AI ​​411 to display specific examination and treatment methods, locations, and points to note in images or videos when planning examinations and treatments, and to show them to the practitioner. This allows the AI ​​to answer the practitioner's questions and provide detailed information as requested.

[0036] As a vision system, it detects position, tilt angle, and direction, for example, in an examination of upper limb raising and lowering movements, it monitors the position and movement (stiffness, movement speed and smoothness) to detect whether the movement is unnatural. If unnatural load or movement is measured in the movement of the person being treated 2, the system will warn the person being treated 2 or prompt them to stop or release the movement.

[0037] Practitioner 1 attaches each sensor to patient 2 before the treatment. However, depending on the type of sensor, it may not be necessary to attach it to patient 2. For example, the facial recognition system 25 may not be attached to patient 2, but may be placed in the treatment room or on the treatment equipment (ceiling, bed, etc.), or it may be attached to practitioner 1's head or glasses, etc.

[0038] The user of terminal device 30 is practitioner 1. However, the user of terminal device 30 may be someone other than practitioner 1 (for example, an assistant who assists practitioner 1).

[0039] The terminal device 30 connects to each sensor constituting the sensor group 20 via wireless means such as Wi-Fi (trademark) or Bluetooth (trademark), or via wired means such as a USB (Universal Serial Bus) cable, and acquires the biological data measured by each sensor.

[0040] Alternatively, the biological data measured by each sensor may be recorded on a detachable, portable recording medium, and the terminal device 30 may read the measured biological data from the portable recording medium.

[0041] Furthermore, the terminal device 30 connects to the analysis device 40 via a network N, which is a bidirectional communication network such as the Internet. The terminal device 30 transmits the biological data acquired from each sensor to the analysis device 40 and requests analysis. Alternatively, the terminal device 30 and the analysis device 40 may be directly connected by wire or wireless connection without going through the network N. Furthermore, the terminal device 30 and the analysis device 40 may be integrated by incorporating the functions of the analysis device 40, which will be described later, into the terminal device 30.

[0042] The analysis device 40 analyzes the biological data transmitted from the terminal device 30, determines the appropriate treatment content for the patient 2, and transmits it back to the terminal device 30. Furthermore, the terminal device 30 receives the appropriate treatment content for the patient 2 obtained as a result of the analysis of the biological data from the analysis device 40 and presents it to the user (practitioner 1).

[0043] Figure 2 shows an example of the configuration of the functional blocks of the terminal device 30. The terminal device 30 has the following functional blocks: a data acquisition unit 31, an analysis request unit 32, a storage unit 33, a communication unit 34, and a display unit 35.

[0044] The terminal device 30 consists of a computer such as a smartphone, a tablet PC (Personal Computer), a notebook PC, a desktop PC, a dedicated terminal, etc., which includes a processor such as a CPU (Central Processing Unit), a memory such as a DRAM (Dynamic Random Access Memory), a storage such as a HDD (Hard Disk Drive) or SSD (Solid State Drive), an input device such as a keyboard, a mouse, a touch panel, etc., an output device such as a display, and a communication module such as a NIC (Network Interface Card) (none of which are shown).

[0045] The data collection unit 31 and the analysis request unit 32 are realized by the processor of the computer executing a predetermined program. The data collection unit 31 is connected to each sensor of the sensor group 20 via the communication unit 34, collects the measured biological data, and stores it in the storage unit 33. Also, the data collection unit 31, for example, acquires attribute information (such as age, gender, height, weight, occupation, working hours, free time, address, types of surgeries with experience, etc.), health status, past history, exercise history, exercise frequency, alcohol intake, smoking history, sleep status, current symptoms, etc. from the subject 2 before and after the treatment in a questionnaire manner and records them in the storage unit 33. Note that the input of attribute information, etc. by the subject 2 may be done using the subject 2's smartphone, etc. (not shown). Also, the attribute information may include the execution status of the instructed self-care when the treatment is being received continuously.

[0046] The analysis request unit 32 transmits the biological data and attribute information, etc. stored in the storage unit 33 to the analysis device 40 via the communication unit 34 and the network N to request an analysis.

[0047] The storage unit 33 consists of the memory and storage of the computer. The storage unit 33 stores the biological data, attribute information, etc. collected from each sensor of the sensor group 20 by the data collection unit 31.

[0048] The communication unit 34 consists of the communication module of the computer. The communication unit 34 is connected to each sensor of the sensor group 20 wirelessly or by wire to receive biological data. Also, the communication unit 34 is connected to the analysis device 40 via the network N to communicate various data.

[0049] The display unit 35 consists of the output device of the computer. The display unit 35 displays an operation screen for the user (practitioner 1). On the operation screen, in addition to displaying a payment instruction to the user, instructions may be given using voice or sound. Furthermore, responses from the user to the instructions may be accepted.

[0050] Figure 3 shows a configuration example of the functional blocks of the analysis device 40. The analysis device 40 has functional blocks of a data analysis unit 41 and a communication unit 42. The analysis device 40 is, for example, a computer such as a server computer including a processor such as a CPU, a memory such as a DRAM, a storage such as an HDD or SSD, an input device such as a keyboard, a mouse, a touch panel, an output device such as a display, and a communication module such as a NIC (none of which are shown).

[0051] The data analysis unit 41 is realized by the processor of the computer executing a predetermined program. The data analysis unit 41 requests the AI (Artificial Intelligence) 411 to analyze the biological data transmitted from the terminal device 30, and obtains, as the analysis result, the health state of the subject 2, the evaluation of the treatment (presence or absence of pain, discomfort, pleasure, etc.), and the treatment content suitable for the subject 2.

[0052] Specifically, AI411 analyzes biometric data using AI algorithms (MLP, CNN, clustering / anomaly detection algorithms, etc.) to plan treatment content and quantify the effects before and after treatment. This quantification incorporates feedback from the treatment recipient's evaluation results and personal data obtained from post-treatment questionnaires. Furthermore, based on the facial recognition results of the treatment recipient, pain, discomfort, pleasure, and unnatural movements are detected in real time. If pain or other issues are detected during treatment, a warning is immediately issued to the practitioner. In addition, the system learns from past treatment content and corresponding effect data, as well as subjective opinions from the treatment recipient, to automatically generate treatment content (treatment plan) for subsequent sessions.

[0053] The data analysis unit 41 can acquire attribute information of the person receiving treatment 2 by receiving input from the person receiving treatment 2 via the terminal device 30. The data analysis unit 41 may also input the attribute information of the person receiving treatment 2 into the AI ​​411 and have it formulate a self-care plan suitable for the person receiving treatment 2 to implement themselves after treatment by the practitioner 1.

[0054] Furthermore, the data analysis unit 41 instructs the AI ​​411 to evaluate the effectiveness of the treatment and the duration of its effects by comparing the biometric data measured from the patient 2 before the treatment with the biometric data measured from the patient 2 after the treatment, and by comparing the biometric data measured from the patient 2 after the previous treatment with the biometric data measured from the patient 2 before the treatment. The AI ​​411 then provides feedback to the practitioner 1 based on the evaluation obtained. This allows the practitioner 1 to modify the treatment content or consider the treatment cycle.

[0055] Generally, treatments are often received on a continuous basis. Therefore, the AI ​​411 used by the data analysis unit 41 may be designed to clearly define the positioning of the current treatment and subsequent treatments, based on the premise of continuous treatment, and output the reasons for the design so that the practitioner 1 can explain them to the patient 2. Furthermore, if the patient 2 is not satisfied with the design of continuous treatment (for example, the number of treatments, duration, date, etc.), alternative options and differences in the effectiveness and effects of the predicted course of action of the alternative options may be output. In addition, warnings about possible adverse reactions, side effects, healing reactions, and other temporary symptoms that may occur as a result of the current treatment may be output and presented to the practitioner 1 as needed.

[0056] The communication unit 42 consists of the computer's communication module. The communication unit 42 connects to the terminal device 30 via the network N and communicates various data. For example, the communication unit 42 receives an analysis request and the patient's biological data from the terminal device 30. The communication unit 42 also transmits to the terminal device 30 the treatment content suitable for the patient 2 generated by the AI ​​411, an evaluation of the treatment's effectiveness, and a self-care plan formulated for the patient 2.

[0057] <Treatment Optimization Process by Treatment Optimization System 10> Figure 4 is a flowchart illustrating an example of treatment optimization processing by the treatment optimization system 10. This treatment optimization process is started in response to a predetermined operation from the user to the terminal device 30.

[0058] As a prerequisite, it is assumed that attribute information, health status, medical history, current symptoms, etc., obtained from the patient 2 through a questionnaire are already stored in the memory unit 33.

[0059] First, the practitioner 1 attaches each sensor constituting the sensor group 20 to the person being treated 2, or places them in a position suitable for measurement (step S1).

[0060] Next, the data acquisition unit 31 of the terminal device 30 instructs each sensor constituting the sensor group 20 to take measurements via the communication unit 34, and collects the biological data measured by each sensor and stores it in the storage unit 33 (step S2).

[0061] Next, the analysis request unit 32 reads the biological data and attribute information stored in the storage unit 33 and transmits it to the analysis device 40 via the communication unit 34 and the network N to request analysis. In response to this request, the data analysis unit 41 of the analysis device 40 instructs the AI ​​411 to analyze the biological data transmitted from the terminal device 30. The AI ​​411 takes the biological data of the person being treated 2 as input, estimates the health status of the person being treated 2, and plans a treatment plan suitable for the person being treated 2 based on the estimation result (step S3).

[0062] Next, the communication unit 42 transmits the results of the AI ​​411's analysis, namely the patient's health condition and the appropriate treatment plan for patient 2, to the terminal device 30. The display unit 35 of the terminal device 30 then visualizes and displays the patient's health condition and the appropriate treatment plan on the screen for the user (practitioner 1) (step S4). Practitioner 1 then begins treatment on patient 2 according to the presented information regarding patient 2's health condition and the appropriate treatment plan.

[0063] Next, the data acquisition unit 31 of the terminal device 30 instructs each sensor of the sensor group 20 to take measurements from the patient 2 during the procedure, and collects the biological data measured by each sensor and stores it in the storage unit 33 (step S5).

[0064] Next, the analysis request unit 32 reads the biological data stored in the memory unit 33 and transmits it to the analysis device 40 via the communication unit 34 and the network N to request analysis. In response to this request, the data analysis unit 41 of the analysis device 40 instructs the AI ​​411 to analyze the biological data transmitted from the terminal device 30. The AI ​​411 takes the biological data of the person being treated 2 as input and plans the evaluation indicators for the treatment of the person being treated 2 (for example, presence or absence of pain, numbness, pressure, weakness, presence or absence of discomfort, change in discomfort, resolution of discomfort, pleasure, etc.) and the treatment content modified according to the evaluation indicators of the person being treated 2 (step S6).

[0065] Next, the communication unit 42 transmits the evaluation index of the patient 2, which is the result of the analysis by the AI ​​411, and the treatment content modified according to the evaluation index of the patient 2 to the terminal device 30. The display unit 35 of the terminal device 30 visualizes and displays the evaluation index of the patient 2 and the appropriate treatment content on the screen, providing feedback to the user (practitioner 1) (step S7). Practitioner 1 appropriately modifies the treatment for the patient 2 according to the presented evaluation index of the patient 2 and the modified treatment content.

[0066] After the procedure is completed, the data acquisition unit 31 of the terminal device 30 then instructs each sensor of the sensor group 20 to take measurements from the patient 2 after the procedure, and collects the biological data measured by each sensor and stores it in the storage unit 33 (step S8).

[0067] Next, the analysis request unit 32 reads the biological data stored in the storage unit 33 and transmits it to the analysis device 40 via the communication unit 34 and the network N to request analysis. In response to this request, the data analysis unit 41 of the analysis device 40 instructs the AI ​​411 to analyze the biological data transmitted from the terminal device 30. The AI ​​411 takes the biological data of the person being treated 2 as input and obtains an evaluation index for the effectiveness of the treatment on the person being treated 2 (step S9). The evaluation index for the effectiveness of the treatment on the person being treated 2 may be fed back to the practitioner 1.

[0068] Next, the communication unit 42 stores the evaluation index for the effectiveness of the treatment on the patient 2, which is the result of the analysis by the AI ​​411, and reflects it in planning the treatment content for the next treatment (step S10). With this, the treatment optimization process by the treatment optimization system 10 is completed.

[0069] According to the treatment optimization process described above, the practitioner 1, who is the user of the terminal device 30, can obtain treatment content suitable for the health condition of the patient 2 before treatment. In addition, the practitioner 1 can check the evaluation indicators for the treatment given to the patient 2 in real time during treatment, modify the treatment content according to the condition of the patient 2, and enhance the effectiveness of the treatment while reducing the burden on the patient 2. Furthermore, the practitioner 1 can confirm the effect of the treatment on the patient 2.

[0070] <Modification> Before and after treatment, movement tests, joint tests, and neurological / orthopedic tests may be performed on the patient 2, and the results of the tests may be analyzed by AI 411 and used to plan the treatment content, etc.

[0071] In the movement test, for example, subject 2 is asked to perform movements such as forward and backward flexion and twisting, as well as left and right flexion, as well as upper and lower limb movements, as well as lifting, lowering, flexion, twisting, and rotation, as well as body and spine movements, as well as forward flexion, backward flexion, lateral flexion, and rotation. Data from each movement test is then collected using a movement detection sensor.

[0072] For example, data from forward flexion can be used to assess the flexibility of the lower back, quadriceps, hamstrings, etc. Data from backward flexion can be used to assess the range of motion and muscle strength of the lower back. Data from side flexion can be used to assess the flexibility of the sides of the body and the range of motion of the lower back.

[0073] Joint examinations detect joint movements such as the range of motion of the hands and arms, and flexion and extension of the elbows and knees. For example, data on the range of motion of the hands and arms can be used to evaluate the range of motion of joints such as the shoulders, elbows, and wrists.

[0074] Neurological and orthopedic examinations include performing tests such as the straight leg raise (SLR) test, leg length discrepancy test, and Patrick test, and collecting data using electromyography sensors.

[0075] In addition to the tests described above, the patient 2 may also be asked to perform their own tests. Figures 5 to 7 show examples of these tests.

[0076] For example, as shown in Figure 5, with the patient 2 with their back, the back of their head, and palms against the wall behind them, they are asked to try to raise their arms upwards by sliding them from bottom to side while keeping their arms extended. This allows for an examination of the flexibility and stiffness of the shoulders and shoulder blades. During this time, it is checked whether the back and the back of the head are not lifting away from the wall, and whether the palms are lifting away from the wall. It is also checked whether the neck and spine are tilting to the opposite side when raising the arms. Furthermore, it is checked from the facial expression and the smoothness of the hand movements to see if the patient is trying to raise their arms despite feeling pain.

[0077] Figure 6 shows the state in which marks 100 are placed on the forehead, temples, back of the hands, elbows, shoulders, chest, greater trochanter, knees, ankles, etc., of the patient 2 during the examination shown in Figure 5. By placing marks 100 on various parts of the patient 2's body, the detection accuracy of the motion detection sensor can be improved. The marks 100 may also have patterns (such as grid patterns) or reflectors attached to them that make it easy to recognize the tilt in the three-dimensional direction. Alternatively, a cutout symbol such as a QR code (trademark) may be provided to determine the angle.

[0078] Furthermore, as shown in Figure 6, vertical and horizontal lines may be provided on the wall behind the person being treated 2 to make it easier to detect the tilt of the body (including partial tilts). These vertical lines can be drawn on the wall, printed on paper, or projected using images or laser light.

[0079] Figure 7 shows an example of a display on the display unit 35 of the terminal device 30 showing the predicted skeletal condition of the patient 2 based on the results of the unique examination. In the example shown in the figure, the main skeletal structures shown are the spine, scapula, pelvis, arms, legs, etc. In addition to the skeleton, the display may also show, for example, the condition of internal organs compressed by scoliosis, or the bones, muscles, ligaments, nerves, internal organs, etc. that are likely to be damaged or painful under load, and display warnings, cautions, etc. regarding the predicted condition (compression, damage, pain, etc.) using images or text.

[0080] In this way, when planning a treatment based on the condition of patient 2 analyzed by AI411, it is believed that the effectiveness of the treatment will be further improved by making it easier for both the practitioner 1 and the patient to understand the current situation.

[0081] The health status of the patient 2, analyzed by AI 411, may be shared with a specialist located remotely, allowing the patient to receive expert diagnoses and treatment suggestions. For example, the health status information may be displayed on a terminal accessible to the specialist, and the patient can receive diagnoses and treatment suggestions entered by the specialist. The AI ​​411 used by the data analysis unit 41 of the analysis device 40 may not be built into the analysis device 40, but may be provided outside the analysis device 40.

[0082] AI 411 may be a so-called generative AI. For example, a generative AI is a system connected to a communication network that uses machine learning and natural language processing to generate and output various documents, images, videos, audio, program code, etc., in response to instructions (prompts). If an API (Application Programming Interface) is provided for the generative AI, the data analysis unit 41 can also access the API function to input instructions. The generative AI may be built locally and accessed by the analysis device 40 via a LAN (Local Area Network), or it may be accessed via a WAN (Wide Area Network). For example, OpenAI's CHAT-GPT, Google's Gemini, etc., can be used for the generative AI.

[0083] Alternatively, the data analysis unit 41 of the analysis device 40 may be moved to the terminal device 30, and the treatment optimization system 10 may be realized using only the terminal device 30. Or, if the terminal device 30 is a smartphone or tablet computer, an application program may be installed on the smartphone or tablet computer acting as the terminal device 30 to realize each of the above-mentioned functional units. Alternatively, all functional units may be held by the analysis device 40 (server), and information may be presented to the user and instructions may be accepted via a general-purpose browser executed on the terminal device 30. Furthermore, generation AI, AGI, and ASI, which are expected to be newly developed in the future, may also be used.

[0084] Furthermore, the treatment optimization system 10 may communicate and cooperate with external AI systems (such as AI servers or robot systems with built-in AI). It may also have interfaces and protocols for cooperation (such as APIs, messaging protocols, gRPC, WebSockets, and file sharing). This allows the AI ​​411 (self-AI) of the treatment optimization system 10 to have expert AIs knowledgeable in the necessary fields (AIs excelling in areas such as medicine, law, and the latest information) provide answers. In this case, the expert AI becomes the primary AI, and the self-AI becomes secondary. Conversely, for example, the self-AI may become the primary AI and give instructions to a work AI robot. The self-AI may then output answers to requests (questions) from the work AI robot.

[0085] The present invention is not limited to the embodiments and modifications described above, and various further modifications are possible. For example, the embodiments and modifications described above are explained in detail for the purpose of clearly illustrating the present invention, and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to replace parts of one modification with other modifications or to combine modifications.

[0086] Furthermore, each of the above configurations, functions, processing units, and processing means may be implemented in hardware, either partially or entirely, by designing them as integrated circuits, for example. Alternatively, each of the above configurations and functions may be implemented in software by a processor interpreting and executing programs that implement each function. Information such as programs, tables, and files that implement each function can be stored in memory, recording devices such as hard disks and SSDs, or recording media such as IC cards, SD cards, and DVDs. Also, control lines and information lines are shown only if deemed necessary for explanation, and not all control lines and information lines are necessarily shown in the actual product. In practice, it can be assumed that almost all configurations are interconnected.

[0087] The present invention can be provided in various forms, not limited to systems or devices, but also including methods, computer-readable programs, and so on.

[0088] 1...Practitioner, 2...Patient, 10...Treatment optimization system, 20...Sensor group, 21...Wearable sensor, 22...Vision system, 23...Physiological sensor, 24...Tactile sensor, 25...Facial recognition system, 30...Terminal device, 31...Data acquisition unit, 32...Analysis request unit, 33...Storage unit, 34...Communication unit, 35...Display unit, 40...Analysis device, 41...Data analysis unit, 42...Communication unit, 100...Mark

Claims

1. A treatment optimization system comprising: a data collection unit for collecting the biological data of a person receiving treatment; a data analysis unit for having an AI trained by machine learning analyze the biological data before treatment to plan a treatment plan suitable for the person receiving treatment; having the AI ​​analyze the biological data of the person receiving treatment during treatment to estimate at least one of the following as evaluation indicators for the person receiving treatment: pain, numbness, pressure, weakness, presence or absence of discomfort, change in discomfort, resolution of discomfort, and pleasure; and having the AI ​​plan a modified treatment plan according to the estimated evaluation indicators of the person receiving treatment; having the AI ​​analyze the biological data of the person receiving treatment after treatment to estimate the effect of the treatment and reflect this in planning the treatment plan for the person receiving treatment next time; and a display unit for presenting the treatment plan planned by the AI ​​to the practitioner.

2. A treatment optimization system according to claim 1, wherein the data analysis unit causes the AI ​​to output the reasons for planning the treatment so that the practitioner can explain to the patient the purpose of the current treatment and the purpose of subsequent treatments, assuming continuous treatment, or to output a non-continuous alternative to continuous treatment if the patient is not convinced by the plan for continuous treatment, and to output the difference between the expected effect when the continuous treatment is performed on the patient and the expected effect when the alternative is performed on the patient.

3. A treatment optimization system according to claim 1, wherein the data analysis unit inputs at least one of the following to the AI ​​along with the biometric data: attribute information of the person to be treated, health status, medical history, and current symptoms, and causes the AI ​​to formulate a treatment plan suitable for the person to be treated.

4. A treatment optimization system according to claim 1, wherein the data analysis unit causes the AI ​​to plan a treatment that promotes the secretion of hormones in the person receiving treatment.

5. A treatment optimization system according to claim 1, wherein the data collection unit collects, as the biological data, physical data before and after treatment that can be measured using a wearable sensor, data representing changes in posture and movement that can be measured using a vision system, internal physiological data that can be measured using a physiological sensor, tactile data and pressure data that can be measured using a tactile sensor, and at least one of the movements of the person receiving treatment and facial expressions including conversation and the content of conversation that can be measured using an facial expression recognition system.

6. A treatment optimization system according to claim 1, characterized in that the AI ​​analysis results are shared with a specialist other than the practitioner, and at least one of a professional diagnosis and a treatment proposal is received from the specialist.

7. A treatment optimization method using a treatment optimization system including a computer, comprising: a data collection step of collecting the subject's biological data using the computer; having an AI trained by machine learning analyze the biological data before treatment to plan a treatment plan suitable for the subject; having the AI ​​analyze the subject's biological data during treatment to estimate at least one of the subject's evaluation indicators for treatment, such as pain, numbness, pressure, weakness, discomfort, change in discomfort, relief of discomfort, or pleasure, and then planning a modified treatment plan according to the estimated evaluation indicators of the subject; having the AI ​​analyze the subject's biological data after treatment to estimate the effect of the treatment and reflect this in planning the next treatment plan for the subject; and a display step of presenting the treatment plan planned by the AI ​​to the practitioner.

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