Ai-based diagnostic system using tenderness and diagnosis method therefor

Abstract

One embodiment of the present invention provides an AI-based diagnostic system using tenderness and a diagnostic method therefor, the diagnostic system comprising: a pressure sensor unit worn by a practitioner to measure the pressure applied to a tender point of a recipient; a speech acquisition unit for transferring a pain report of the recipient when the pressure is generated from the pressure sensor unit; an image acquisition unit for capturing an image of body information about the recipient and the position of the pressure sensor unit; a display unit for displaying the position of the tender point by means of the image captured by the image acquisition unit, and displaying, by means of the image captured by the image acquisition unit, the pain report reported from the recipient according to the position of the tender point, the pressure measured at the tender point, and the pressure applied to the tender point; and a server for analyzing the current state or treatment progress of the recipient by using data transmitted from the pressure sensor unit, the image acquisition unit, and the speech acquisition unit, predicting a treatment effect, converting the current state or treatment progress of the recipient into numbers, graphs, or pictures, and transmitting the numbers, graphs, or pictures to the display unit.

Description

AI-based diagnostic system using tenderness and the diagnostic method thereof

[0001] The present invention relates to a medical diagnostic system and a diagnostic method thereof, and more particularly to an AI-based diagnostic system and diagnostic method utilizing tenderness that can visually display diagnosis, treatment, and treatment effects by analyzing pressure applied to acupoints and the resulting tenderness using AI on a glove, thimble, or sticker equipped with a pressure sensor.

[0002] Generally, acupoints are associated with the treatment of specific parts of the body, and ongoing efforts are being made to utilize acupoints or tender points for patient diagnosis. Trigger points (TPs), which are utilized not only in Korean medicine clinics but also in general clinics, physical therapy centers, and exercise and rehabilitation centers, are points in specific muscles or fascia that induce pain; clinically, they serve as both diagnostic and therapeutic points. While various devices such as pressure gauges, palpation meters, and abdominal palpation devices have been developed for existing musculoskeletal diagnosis, these devices simply measure pressure in a specific area or use mechanical devices to evaluate applied pressure. These devices suffer from reduced repeatability and reproducibility of measurements due to variations in the reproducibility of the pressure application location, speed, and duration, as well as the non-standardization of pain response recordings. Furthermore, the reliability of results can vary depending on the individual characteristics of the patient and practitioner, making it difficult to obtain highly reliable results as these individual traits are reflected.

[0003] Conventional Korean Registered Patent No. 10-1557786 (Device for Measuring Swelling and Tenderness in Arthritis) discloses a technology that improves the accuracy of arthritis diagnosis by measuring swelling and tenderness in the joint areas of arthritis patients. Korean Registered Patent No. 10-1557786 consists of a glove-shaped device in which a plurality of joint parts inserted into a finger are formed in a glove shape, and which measures the outer circumference of the finger and measures and displays the pressure in that area. Korean Registered Patent No. 10-1557786 is equipped with an input section that allows the user to input whether they feel pain when the pressure on the finger increases and the user feels pain; however, since the user's pain is input based on subjective feelings, there may be a problem of low accuracy.

[0004] A prior art document in this technical field is Korean Registered Patent Publication No. 10-1557786 (November 20, 2020).

[0005] The technical problem to be solved by the present invention is to provide an AI-based diagnostic system using tenderness and a diagnostic method that can simultaneously perform diagnosis, treatment, and prediction of treatment effects by measuring pressure applied to an acupoint through a pressure sensor wearable by a practitioner, and simultaneously collecting and analyzing the patient's pain as video or audio when the target acupoint is pressed. The collection of pain includes voice or video-based pain expressions, and in some cases, may further include at least one of biosignals such as pulse waves, skin reactions, and electromyography signals.

[0006] The technical problems that the present invention aims to solve are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art to which the present invention belongs from the description below.

[0007] To achieve the above technical objectives, according to one embodiment of the present invention, an AI-based diagnostic system using tenderness may be provided, comprising: a pressure sensor unit for measuring pressure applied to a tender point of a patient when worn by a practitioner; a voice acquisition unit for transmitting a pain report from the patient when pressure is generated at the pressure sensor unit; an image acquisition unit for capturing the body information of the patient and the location of the pressure sensor unit; a display unit for displaying a pain report reported by the patient according to the location of the tender point, the pressure measured at the tender point, and the pressure applied to the tender point through the image captured by the image acquisition unit; and a server that analyzes the current state or treatment progress of the patient using data transmitted from the pressure sensor unit, the image acquisition unit, and the voice acquisition unit, predicts the treatment effect, converts the current state or treatment progress of the patient into numbers, graphs, or pictures, and transmits them to the display unit.

[0008] The pressure sensor unit may include an outer shell in the form of a glove or thimble; an elastic electrode coated on the inner or outer side of the outer shell; a pressure sensor connected to the elastic electrode; and a wireless communication unit that transmits a signal measured by the pressure sensor.

[0009] The above image acquisition unit may include a camera worn on the temporal region of the operator or installed in the operating space.

[0010] The server may further include a location recognition module that uses image data provided by the image acquisition unit to map the location of the pressure applied by the operator onto the body map of the subject and provide it to the display.

[0011] The server may further include a progress analysis module that accumulates pain reports reported by the subject according to the pressure applied to the tender point and predicts the degree of pain relief of the subject using the accumulated information.

[0012] The server may further include a waveform analysis module that receives a pulse wave signal input through the pressure sensor unit and provides the pulse wave signal to the display.

[0013] The waveform analysis module above can analyze pain rhythm and intensity from the pulse wave signal.

[0014] To achieve the above technical objective, according to another embodiment of the present invention, a diagnostic method using AI-based tenderness may be provided, comprising: receiving location information of a pressure sensor unit that applies pressure to a tender point or acupoint of a patient from captured image data; receiving information on the magnitude and duration of pressure applied by the pressure sensor unit; receiving pain report information from the patient when pressure is applied to the tender point or acupoint; mapping pain intensity information corresponding to the magnitude and duration of pressure at the tender point location; a data analysis step of analyzing the mapped information to analyze the current state or treatment progress of the patient and predict the treatment effect; and a data visualization step of converting the analyzed data into a graph, number, or picture and displaying it.

[0015] The above data analysis step can analyze the pattern of pressure change according to the magnitude and duration of the pressure, the temporal change of the pain response corresponding to the pattern of pressure change, and the correlation between the location and intensity of the pain.

[0016] The step of receiving location information of the pressure sensor unit may include the step of mapping the location of the pressure applied by the operator to the body map of the subject using the image data.

[0017] The AI-based diagnostic system and diagnostic method using tenderness according to embodiments of the present invention can improve the accuracy and reliability of the diagnosis by quantitatively measuring the pressure and tenderness response applied to a patient's acupoints.

[0018] In addition, the AI-based diagnostic system and diagnostic method according to an embodiment of the present invention can provide patient-tailored diagnosis and treatment by predicting the course of treatment through AI learning.

[0019] In addition, the AI-based acupoint tenderness diagnostic system and diagnostic method according to an embodiment of the present invention can enhance treatment effectiveness by allowing patients and doctors to intuitively understand the data using a visualization device.

[0020] The effects of the present invention are not limited to the effects described above, and should be understood to include all effects that can be inferred from the composition of the invention described in the description or claims of the present invention.

[0021] FIG. 1 is a system diagram illustrating a diagnostic system using AI-based tenderness according to an embodiment of the present invention.

[0022] Figure 2 is a block diagram illustrating the components of Figure 1.

[0023] Figure 3 is a diagram illustrating, as an example, how localized tenderness is quantified and displayed in the form of a graph in a diagnostic system using AI-based tenderness according to an embodiment of the present invention.

[0024] FIG. 4 is an example illustrating the main components of the pressure sensor unit shown in FIG. 1.

[0025] FIG. 5 is a flowchart sequentially illustrating a diagnostic method using AI-based tenderness according to an embodiment of the present invention.

[0026] The present invention will be described below with reference to the attached drawings. However, the present invention may be implemented in various different forms and is therefore not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification have been given similar reference numerals.

[0027] Throughout the specification, when it is stated that a part is "connected (connected, in contact, combined)" with another part, this includes not only cases where they are "directly connected," but also cases where they are "indirectly connected" with other members interposed between them. Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but rather allows for the inclusion of additional components.

[0028] The terms used herein are merely for describing specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, terms such as “comprising” or “having” are intended to indicate the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0029] Embodiments of the present invention will be described in detail below with reference to the attached drawings.

[0030] FIG. 1 is a system diagram illustrating a diagnostic system using AI-based tenderness according to an embodiment of the present invention, and FIG. 2 is a block diagram for explaining the components of FIG. 1.

[0031] Referring to FIGS. 1 and 2, an AI-based diagnostic system using pressure according to an embodiment of the present invention may include a pressure sensor unit (200), an image acquisition unit (300), a voice acquisition unit (400), a display unit (600), and a server (500).

[0032] Specifically, the pressure sensor unit (200) is formed in the shape of a glove or thimble worn by the practitioner and can measure pressure applied to the patient's pressure points or acupoints. The pressure sensor unit (200) is connected to a pressure sensor and an elastic electrode so that the characteristics of the pressure sensor do not change even when the practitioner moves freely. The configuration of the pressure sensor unit (200) will be further explained through FIG. 4.

[0033] The pressure sensor unit (200) can transmit the pressure applied to the pressure point or acupoint to the server (500) in real time. The pressure sensor unit (200) can transmit the magnitude and duration of the applied pressure to the server (500).

[0034] Additionally, the pressure sensor unit (200) can measure the waveform of the pulse wave when measuring the pulse wave at a place where the pulse wave can be measured, such as a radial artery (during pulse diagnosis) and transmit it to the server (500).

[0035] The image acquisition unit (300) captures an image from at least one camera. The image acquisition unit (300) may be worn on the temporal region of the operator or installed on the ceiling or wall of the operating room.

[0036] The image acquisition unit (300) may use a device capable of capturing 3D images. The images captured by the image acquisition unit (300) are transmitted to the server (500) in real time.

[0037] The voice acquisition unit (400) can transmit the pain level verbally reported by the patient to the server (500). For example, it may be attached integrally to a camera worn by the patient or installed near the patient. The voice acquisition unit (400) can convert the pain level verbally reported by the patient into data using voice recognition technology and transmit it to the server (500).

[0038] The display unit (600) may use a conventional display device such as a monitor, mobile terminal, or TV, or a display device in the form of glasses worn by the operator.

[0039] The display unit (600) can display acupoints or pressure points in the human body model. The display unit (600) can display information such as acupoints or pressure points and the degree of pain at the pressure points (numerical display) in the human body model transmitted to the server (500). The display unit (600) can display information such as numbers, pictures, and graphs so that the practitioner and the patient can intuitively verify it.

[0040] The display unit (600) may use any display device capable of displaying mixed reality (XR: extended reality). The display unit (600) may be driven by an application, and when preset data is input, it may display it as virtual reality, augmented reality, or mixed reality.

[0041] The display unit (600) can display various information such as the patient's medical records (chart), human body modeling, the patient's acupoints, tender points, and pain information by utilizing digital twin technology.

[0042] The display unit (600) can visually represent pressure and pain data by mapping them onto a patient's body model as shown in FIG. 3. Additionally, the display unit (600) can display a chart showing the trend of change by comparing data before and after treatment, and can display the tenderness intensity in color so that the practitioner and the patient can intuitively check it.

[0043] The server (500) can analyze the current condition and treatment progress of the patient using data transmitted from the pressure sensor unit (200), image acquisition unit (300), and voice acquisition unit (400), predict the treatment effect, and transmit the relevant information to the display unit (600).

[0044] For example, the server (500) can efficiently manage the patient's treatment history by storing or transmitting it to a medical record (chart) program. The server (500) can update the patient's existing medical record program so that it can be used in the future.

[0045] Additionally, the server (500) can provide the analysis results to the EMR (Electronic Medical Record) and chart program of the medical institution. The server (500) can automatically upload the analyzed data by linking it with the chart program (EMR) or transmit it to the medical record system within the medical institution at the request of the practitioner. To this end, the server (500) may be equipped with a communication module that exchanges data with an external chart program via a medical information standard protocol or API, and the practitioner can efficiently manage the patient's treatment progress by reflecting the analysis results in the electronic medical record in real time.

[0046] As illustrated in FIG. 2, the server (500) may include a location recognition module (510), a waveform analysis module (530), and a progress analysis module (520). Additionally, the server (500) may be equipped with memory and may be equipped with a wired or wireless communication device. In the following description, the memory, communication device, etc. included in the server (500) will be omitted as they are in a conventional configuration.

[0047] The location recognition module (510) can recognize the pressure measurement area in a still image or video captured by the image acquisition unit (300). For example, the location recognition module (510) models a three-dimensional captured image and identifies the location of the pressure sensor unit (200) worn by the operator (20). Subsequently, the location recognition module (510) maps the location where the operator (20) applied pressure to the patient (10) onto the modeled human body.

[0048] The location recognition module (510) can recognize the location of a pressure point or acupoint by outputting a three-dimensional image of the patient (10) to a display unit (600) worn by the practitioner (20) and overlaying the location of the pressure sensor unit (200) worn by the practitioner (20).

[0049] Meanwhile, the location recognition module (510) can recognize the acupoints of the patient in advance based on an artificial intelligence-learned database. For example, the location recognition module (510) models the patient (10) by taking a 3D image, and provides coordinate values ​​on the model to objectify the acupoints using a specific program for the objects entered into the coordinate values. Then, if the name is entered into the modeled acupoint, the acupoint can be displayed on the 3D model. This process can be done by the practitioner (20) inputting the name or by using an artificial intelligence algorithm based on a learned database.

[0050] The progress analysis module (520) accumulates pain reports reported by the patient (10) according to the pressure applied to the tender point, and can predict the degree of pain relief of the patient (10) using the accumulated information.

[0051] As shown in FIG. 3, the progress analysis module (520) matches and stores the pain report for each location corresponding to the pressure when the patient (10) reports pain (e.g., intensity 1 to 10) at the location where pressure is applied by the pressure sensor unit (200). The progress analysis module (520) can identify the pain area through the pain report for each tender point of each part.

[0052] Subsequently, the progress analysis module (520) can predict the degree of pain relief in advance by analyzing the pain pattern and correlation using the accumulated data of the patient (10). To predict the degree of pain relief, the progress analysis module (520) may use an artificial intelligence algorithm, and the artificial intelligence algorithm may include machine learning, deep learning, or a combination thereof. For example, the artificial intelligence algorithm may analyze pressure change patterns, temporal changes in pain response, correlations between pain location and intensity, and associations with the patient's medical history and treatment data.

[0053] For example, the progress analysis module (520) may use an artificial intelligence algorithm. The artificial intelligence algorithm can analyze data by combining various neural network models and machine learning. The progress analysis module (520) can analyze pressure and location data using a CNN (Convolutional Neural Network) model to extract spatial features, and analyze temporal data using an RNN (Recurrent Neural Network) or LSTM (Long Short-Term Memory) model to track changes in pain response. The progress analysis module (520) can compare the pre- and post-treatment data of the patient (10) using a random forest and regression model and evaluate whether pain has improved. The progress analysis module (520) can group patient data using a clustering algorithm model to identify patient groups with similar patterns and train them.

[0054] The progress analysis module (520) can evaluate the immediate effect by analyzing pressure and pain response data immediately after treatment, or predict the progress and recovery potential by utilizing previous treatment data.

[0055] The progress analysis module (520) can efficiently manage the patient's treatment history by storing or transmitting it to a medical record (chart) program. The progress analysis module (520) can update the patient's existing medical record program for future use. Additionally, the progress analysis module (520) can provide the analysis results to the medical institution's EMR (Electronic Medical Record) and chart program. The progress analysis module (520) can automatically upload the analyzed data by linking it with the chart program (EMR) or transmit it to the medical record system within the medical institution at the request of the practitioner.

[0056] The waveform analysis module (530) can receive a pulse wave signal input through the pressure sensor unit (200) and analyze the periodic rhythm and variability of the pulse wave. The waveform analysis module (530) can provide the pulse wave signal to the display. The analysis result using the pulse wave signal can be used for pulse diagnosis.

[0057] The waveform analysis module (530) can identify the pain rhythm through FFT (fast Fourier transform) and spectrum analysis. The pulse wave signal is a periodically input signal that has a frequency and a signal magnitude corresponding to the frequency. Therefore, the waveform analysis module (530) can identify the pain rhythm by using the magnitude of the sampled signal through FFT and spectrum analysis.

[0058] Meanwhile, the waveform analysis module (530) can precisely evaluate the diagnostic and treatment effects by visualizing the temporal changes in pain rhythm and intensity and providing them to the display unit (600).

[0059] Figure 4 is a drawing illustrating an example of a pressure sensor unit described with reference to Figures 1 and 2.

[0060] As illustrated in FIG. 4, the pressure sensor unit (200) may be equipped with a wireless communication device, a pressure sensor (210), and a flexible electrode (220).

[0061] Specifically, the wireless communication device can transmit information input from the pressure sensor (210) to the server.

[0062] The pressure sensor (210) measures the magnitude and duration of the pressure when the practitioner presses the pressure point or acupoint of the patient and provides it to the server.

[0063] The flexible electrode (220) can supply power to pressure sensors and wireless communication devices, etc. At this time, when the practitioner presses on the pressure point or acupoint of the patient, deformation of the shape may occur, and when the practitioner's finger joint moves, deformation of the shape may occur. The flexible electrode (220) can maintain the performance of the pressure sensor (210) even if deformation of the shape occurs. For example, the flexible electrode (220) can maintain the performance of the pressure sensor (210) even if deformation of up to 40% occurs.

[0064]

[0065] FIG. 5 is a flowchart sequentially illustrating a diagnostic method using AI-based tenderness according to an embodiment of the present invention.

[0066] FIG. 5 is a diagram illustrating a method of implementing the components of an AI-based pressure-based diagnostic system shown in FIG. 1 to 4 on a server, and will be explained with reference to the components shown in FIG. 1 to 4.

[0067] Referring to FIG. 5, the AI-based diagnostic method using tenderness according to an embodiment of the present invention may include a step of receiving location information of a pressure sensor unit (S100), a step of receiving pressure magnitude and duration information (S200), a step of receiving pain report information (S300), a step of mapping pain intensity of the tenderness point location (S400), a data analysis step (S500), and a data visualization step (S600).

[0068] Specifically, the pressure measurement area recognition step (S100) receives image data captured from a camera located at the operator or in the treatment space from a server. Subsequently, the location of the pressure sensor part is recognized from the image data.

[0069] For example, a method can be used in which a 3D image of the subject is displayed on a display in the form of glasses worn by the operator, and the location of the pressure sensor part is overlaid on the image to determine the location of the pressure sensor part.

[0070] Next, the pressure magnitude and duration information receiving step (S200) can receive information on the magnitude and duration of the pressure applied by the pressure sensor unit from the server.

[0071] Next, the pain report information receiving step (S300) can receive the patient's pain report from the voice acquisition unit on the server.

[0072] Next, the pain intensity mapping step (S400) at the tender point location maps and quantifies the pain report corresponding to the magnitude and duration of pressure at the tender point location.

[0073] For example, when pressure is applied to a patient's acupoint or tender point, pain reports corresponding to the magnitude of the pressure at that location are mapped and stored in a database.

[0074] Next, the data analysis step (S500) analyzes the mapped information to analyze the patient's current condition or treatment progress and predicts the treatment effect. At this time, an artificial intelligence algorithm is used for data analysis. The artificial intelligence algorithm can analyze data by combining various neural network models and machine learning. For example, a CNN (Convolutional Neural Network) model can be used to analyze pressure and position data to extract spatial features, and a RNN (Recurrent Neural Network) or LSTM (Long Short-Term Memory) model can be used to analyze temporal data to track changes in pain response. Additionally, Random Forest and regression models can be used to compare the patient's pre- and post-treatment data and evaluate whether pain has improved. Furthermore, a clustering algorithm model can be used to group patient data to identify patient groups with similar patterns and train the algorithm.

[0075] The data visualization step (S600) converts the analyzed data into a graph, number, or picture and provides it to the display unit. For example, it may quantify and display the pain intensity for each tender point on the patient's body shape, or provide pain reduction over the course of treatment in the form of a graph. Additionally, it may provide the expected treatment end time and effects in the form of a graph.

[0076] The foregoing description of the present invention is for illustrative purposes only, and those skilled in the art will understand that other specific forms can be easily modified without altering the technical spirit or essential features of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single unit may be implemented in a distributed manner, and components described as distributed may likewise be implemented in a combined form.

[0077] The scope of the present invention is defined by the claims set forth below, and all modifications or variations derived from the meaning and scope of the claims and equivalent concepts thereof should be interpreted as being included within the scope of the present invention.

[0078] The modes for carrying out the invention are described together in the best mode for carrying out the invention above.

[0079] The present invention relates to a medical diagnostic system utilizing tenderness and a diagnostic method thereof. In particular, it relates to an AI-based diagnostic system utilizing tenderness and a diagnostic method capable of visually demonstrating diagnosis, treatment, and treatment effects by AI analyzing pressure applied to acupoints and the resulting tenderness using a glove, thimble, or sticker equipped with a pressure sensor. The invention has industrial applicability in medical-related industries.

Claims

1. A pressure sensor unit worn by a practitioner to measure pressure applied to a patient's tender point; A voice acquisition unit that transmits a pain report from the subject when pressure is generated at the pressure sensor unit above; An image acquisition unit that captures the body information of the subject and the location of the pressure sensor unit; A display unit that displays a pain report reported by the patient according to the location of the pressure point, the pressure measured at the pressure point, and the pressure applied to the pressure point through an image captured by the image acquisition unit; and An AI-based diagnostic system using tenderness comprising a server that analyzes the current state or treatment progress of a patient using data transmitted from the pressure sensor unit, image acquisition unit, and voice acquisition unit, predicts the treatment effect, converts the patient's current state or treatment progress into a number, graph, or picture, and transmits it to the display unit.

2. In Paragraph 1, The above pressure sensor part Glove or thimble-shaped outer shell; A flexible electrode coated on the inner or outer side of the above outer shell; A pressure sensor connected to the above-mentioned flexible electrode; and AI-based diagnostic system using pressure, comprising a wireless communication unit that transmits a signal measured by the pressure sensor.

3. In Paragraph 1, The above image acquisition unit An AI-based diagnostic system utilizing tenderness comprising a camera worn on the temporal region of the operator or installed in the procedure space.

4. In Paragraph 1, The above server is An AI-based diagnostic system using tenderness, further comprising a location recognition module that maps the location of pressure applied by the practitioner to the body map of the subject using image data provided by the image acquisition unit and provides it to the display.

5. In Paragraph 1, An AI-based diagnostic system using tenderness, further comprising a progression analysis module that accumulates pain reports reported by the subject according to pressure applied to the tender point and predicts the degree of pain relief of the subject using the accumulated information.

6. In Paragraph 1, The above server is A diagnostic system using AI-based pressure pain, further comprising a waveform analysis module that receives a pulse wave signal input through the pressure sensor unit and provides the pulse wave signal to the display.

7. In Paragraph 6, The waveform analysis module above is an AI-based diagnostic system using tenderness that analyzes pain rhythm and intensity from the pulse wave signal.

8. As a diagnostic method using AI-based tenderness, A step of receiving location information of a pressure sensor unit that applies pressure to a tender point or acupoint of a patient from captured video data; A step of receiving information on the magnitude and duration of the pressure applied by the pressure sensor unit; A step of receiving pain report information from the subject when applying pressure to the above tender point or acupoint; A step of mapping pain intensity information corresponding to the magnitude and duration of pressure at the above-mentioned tender point location; A data analysis step for analyzing the mapped information to analyze the current state or treatment progress of the subject and predicting the treatment effect; and A diagnostic method using AI-based tenderness that includes a data visualization step of converting the above-mentioned analyzed data into a graph, number, or picture and displaying it.

9. In Paragraph 8, The above data analysis step is A diagnostic method using AI-based tenderness that analyzes the pattern of pressure change according to the magnitude and duration of the pressure, the temporal change of the pain response corresponding to the pattern of pressure change, and the correlation between the location and intensity of the pain.

10. In Paragraph 8, The step of receiving location information of the pressure sensor unit above A diagnostic method using AI-based tenderness that includes the step of mapping the location of pressure applied by the practitioner to the body map of the subject using the above-mentioned image data.

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