An infrared thermography-assisted acupoint selection system for acupuncture treatment of knee osteoarthritis

Abstract

The present invention relates to an infrared thermography-assisted acupoint selection system for acupuncture treatment of knee osteoarthritis, which comprises: an interrogation module for collecting and recording the patient's chief complaint information and transmitting it to the data storage module; an infrared image acquisition module for collecting and storing the infrared images of knee osteoarthritis patients and transmitting them to the data storage module; a pattern library construction module for calling data from the data storage module to construct pattern libraries for different types of knee osteoarthritis patients and transmitting them to the data storage module; an infrared image pattern identification module for calling data from the data storage module to identify the patterns of newly diagnosed knee osteoarthritis patients and transmitting them to the data storage module; and an acupoint recommendation module for calling the pattern identification results and the data of the pattern library from the data storage module to obtain the selection rates of each acupoint by experts within the corresponding pattern in the pattern library, and taking the highest selection rate as the output result of the acupoint recommendation module.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and particularly to an infrared thermography-assisted acupoint selection system for acupuncture treatment of knee osteoarthritis. Background Art

[0002] There are many acupoints for acupuncture treatment of knee osteoarthritis, but clinical physicians' acupoint selection depends on their own clinical experience and is highly subjective. Domestic young acupuncturists, acupuncture beginners or foreign acupuncturists lack clinical acupoint selection experience, which affects the clinical efficacy of acupuncture treatment for knee osteoarthritis. To solve this problem, objective and visible infrared thermography detection equipment can be used to identify the group characteristics of knee osteoarthritis patients in a pattern recognition manner, and at the same time, the acupoint selection rules can be mined according to the acupoint selection results of experts in each group. After a newly diagnosed patient's image is collected, the system can automatically detect the image features, complete pattern matching, and output the acupoint selection results to the software interface in the form of a recommendation index, thereby establishing an auxiliary acupoint selection expert system for acupuncture treatment of knee osteoarthritis. Given that infrared thermography is a non-invasive and non-radiative green examination device, it is safe and harmless to patients and is conducive to popularization and application. Summary of the Invention

[0003] Aiming at the above problems, the purpose of the present invention is to provide an infrared thermography-assisted acupoint selection system for acupuncture treatment of knee osteoarthritis, which can realize pattern recognition of knee osteoarthritis patients and recommendation of acupuncture acupoints.

[0004] To achieve the above purpose, the present invention adopts the following technical solutions: An infrared thermography-assisted acupoint selection system for acupuncture treatment of knee osteoarthritis, which includes: an interrogation module for collecting and recording the patient's main complaint information and transmitting it to the data storage module; an infrared image acquisition module for collecting and storing the infrared images of knee osteoarthritis patients and transmitting them to the data storage module; a pattern library construction module for calling data from the data storage module to construct pattern libraries of different types of knee osteoarthritis patients and transmitting them to the data storage module; an infrared image pattern recognition module for calling data from the data storage module to perform pattern recognition on newly diagnosed knee osteoarthritis patients and transmitting them to the data storage module; an acupoint recommendation module for calling the pattern recognition results and the data of the pattern library from the data storage module to obtain the selection rates of experts for each acupoint within the corresponding pattern in the pattern library, and taking the highest selection rate as the output result of the acupoint recommendation module.

[0005] Furthermore, it further includes an expert acupoint selection result recording module for recording the acupoint selection results of clinical experts for each knee osteoarthritis patient and transmitting them to the data storage module.

[0006] Furthermore, in the pattern library construction module, the construction of pattern libraries of different types of knee osteoarthritis patients includes:

[0007] Group the infrared image data according to the data of knee osteoarthritis patients collected by the medical interview module;

[0008] Extract the infrared image features of each group of people for cluster analysis; wherein the infrared image features include: the maximum, average, and minimum temperatures of each acupoint in the human lower limb image;

[0009] Conduct supervised cluster analysis on each of the infrared image features to determine the confidence interval of each infrared image feature for each group of people, and establish an infrared thermogram feature pattern library for each group of people.

[0010] Furthermore, in the infrared image pattern recognition module, the pattern recognition of newly diagnosed knee osteoarthritis patients includes:

[0011] Extract infrared image features from the infrared images of newly diagnosed patients collected by the infrared image acquisition module;

[0012] Calculate the multi-dimensional Euclidean distance between the infrared image features of the newly diagnosed patients and the constructed pattern library;

[0013] Sort the Euclidean distances calculated for all the infrared image features in ascending order. The smaller the distance, the higher the matching degree, and the larger the distance, the lower the matching degree;

[0014] Output the top five pattern recognition results according to the matching degree between the new patient data and each pattern in the pattern library.

[0015] Furthermore, in the acupoint recommendation module, obtaining the selection rate of each acupoint by experts within the corresponding pattern in the pattern library includes:

[0016] The data storage module stores the acupoint selection data of each patient collected by the expert acupoint selection result recording module. The pattern library construction module statistically processes the acupoint selection data of each patient collected. Different groups of patients correspond to different patterns; the acupoint recommendation module statistically processes and sorts the expert acupoint selection data of different groups of patients to obtain the acupoint selection rates corresponding to different patterns, that is, the selection rate of each acupoint by experts for each knee osteoarthritis patient in each pattern, which is used as the basis for pattern-based acupoint selection recommendation.

[0017] Furthermore, it further includes a patient management module for the daily maintenance of patient information, including adding, deleting, modifying, and / or querying each storage field.

[0018] Furthermore, it further includes an acupuncture prescription record module for recording the acupuncture acupoint information of clinicians for each patient visit, for statistically calculating the accuracy rate of acupoint selection results, and adjusting the acupoint recommendation coefficient according to the accuracy rate of acupoint selection results to achieve feedback optimization of the acupoint recommendation module.

[0019] Due to the above technical solutions adopted by the present invention, it has the following advantages:

[0020] 1. The present invention can collect the interview data and infrared image data of patients with knee osteoarthritis.

[0021] 2. The present invention can identify the pattern of patients with knee osteoarthritis and recommend acupuncture points through infrared images.

[0022] 3. The present invention can record the acupoint selection results of clinicians, and realize the accuracy test of acupoint recommendation results and the feedback optimization of experts' acupoint selection experience. Description of the Drawings

[0023] Figure 1 It is a schematic structural diagram of an infrared thermography-assisted acupoint selection system in an embodiment of the present invention. Detailed Embodiments

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention fall within the scope of protection of the present invention.

[0025] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0026] In an embodiment of the present invention, an infrared thermography-assisted acupoint selection system for acupuncture treatment of knee osteoarthritis is provided. The infrared thermography-assisted acupoint selection system provided in this embodiment can not only be used in the field of traditional Chinese medicine acupuncture treatment of knee osteoarthritis, but also can be applied to other fields of infrared image pattern recognition or traditional Chinese medicine acupuncture expert systems, etc. In this embodiment, taking the infrared thermography-assisted acupoint selection for the treatment of knee osteoarthritis as an example, the infrared thermography recognition part in other fields is not limited. In this embodiment, as Figure 1 shown, the infrared thermography-assisted acupoint selection system includes:

[0027] An interview module, configured to collect and record the main complaint information of the patient and transmit it to the data storage module; wherein, collecting and recording the main complaint information of the patient includes information related to the course of knee osteoarthritis, including pain score, pain location, pain time, and pain nature.

[0028] An infrared image acquisition module, which is used to acquire and store infrared images of knee osteoarthritis patients and transmit them to the data storage module; among them, the infrared images of knee osteoarthritis patients include environmental temperature, environmental humidity, and infrared images.

[0029] A pattern library construction module, which calls data from the data storage module, constructs pattern libraries for different types of knee osteoarthritis patients, and transmits them to the data storage module.

[0030] An infrared image pattern recognition module, which calls data from the data storage module, performs pattern recognition on newly diagnosed knee osteoarthritis patients, and transmits them to the data storage module.

[0031] An acupoint recommendation module, which calls the pattern recognition results and the data of the pattern library from the data storage module, obtains the selection rates of each acupoint by experts within the corresponding pattern in the pattern library, and uses the highest selection rate as the output result of the acupoint recommendation module.

[0032] In a preferred embodiment, the system of the present invention further includes an expert acupoint selection result recording module, which is used to record the acupoint selection results of clinical experts for each knee osteoarthritis patient and transmit them to the data storage module.

[0033] In the above embodiment, in the pattern library construction module, constructing the pattern libraries for different types of knee osteoarthritis patients includes the following steps:

[0034] 1.1) According to the knee osteoarthritis patient data collected by the interrogation module, stratify and group the infrared image data. The stratification factors include gender, age, and affected side, and the grouping factors include pain location and pain nature.

[0035] 1.2) Extract the infrared image features of each group of people for clustering analysis; among them, the infrared image features include: the maximum, average, and minimum temperatures of each acupoint in the human lower limb image.

[0036] 1.3) Perform supervised clustering analysis on each infrared image feature to determine the confidence interval (95% confidence interval) of each infrared image feature of each group of people. Arrange the confidence intervals of each acupoint temperature feature according to the grouping. Each group of people has its own acupoint temperature confidence interval sequence (i.e., pattern), and establish an infrared thermogram feature pattern library for each group of people.

[0037] In the above embodiment, in the infrared image pattern recognition module, performing pattern recognition on newly diagnosed knee osteoarthritis patients includes the following steps:

[0038] 2.1) According to the infrared images of newly diagnosed patients collected by the infrared image acquisition module, extract infrared image features. The infrared features include the maximum, average, and minimum temperatures of each acupoint.

[0039] In this embodiment, the extraction method adopted is: based on the coordinate position of the acupoint clicked on the image, determine the maximum value, average value and minimum value of the temperature at that position.

[0040] 2.2) Calculate the multi-dimensional Euclidean distance between the infrared image features of the newly diagnosed patient and the constructed pattern library to obtain the Euclidean distance;

[0041] 2.3) Sort the Euclidean distances calculated for all infrared image features in ascending order. The smaller the distance, the higher the matching degree, and the larger the distance, the lower the matching degree;

[0042] 2.4) Output the top five pattern recognition results according to the matching degree between the new patient data and each pattern in the pattern library.

[0043] In each of the above embodiments, in the acupoint recommendation module, obtain the selection rate of each acupoint by experts within the corresponding pattern in the pattern library, including: the data storage module saves the acupoint selection data of each patient collected by the expert acupoint selection result recording module, and the pattern library construction module can statistically analyze the acupoint selection data of each patient collected. Different groups of patients correspond to different patterns; the acupoint recommendation module can statistically analyze and sort the expert acupoint selection data of different groups of patients to obtain the acupoint selection rate corresponding to different patterns, that is, the selection rate of each acupoint by experts for each knee osteoarthritis patient in each pattern, as the basis for pattern-based acupoint selection recommendation.

[0044] In a preferred embodiment, the system of the present invention further includes a patient management module for the daily maintenance of patient information, including addition, deletion, modification and / or query of each storage field.

[0045] In a preferred embodiment, the system of the present invention further includes an acupuncture prescription recording module for recording the acupuncture acupoint information of the clinician for each patient visit, for statistically analyzing the accuracy rate of the recommended acupoint selection results, and adjusting the acupoint recommendation coefficient according to the accuracy rate of the acupoint selection results to achieve feedback optimization of the acupoint recommendation module

[0046] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An infrared thermography-assisted acupoint selection system for acupuncture treatment of knee osteoarthritis, characterized in that, Including: An interrogation module, which is used to collect and record the chief complaint information of patients and transmit it to the data storage module; An infrared image acquisition module, which is used to collect and store the infrared images of patients with knee osteoarthritis and transmit them to the data storage module; A pattern library construction module, which calls data from the data storage module to construct a pattern library for different types of patients with knee osteoarthritis and transmits it to the data storage module; An infrared image pattern identification module, which calls data from the data storage module to identify the patterns of newly diagnosed patients with knee osteoarthritis and transmits it to the data storage module; An acupoint recommendation module, which calls the pattern identification results and the data of the pattern library from the data storage module to obtain the selection rates of experts for each acupoint within the corresponding pattern in the pattern library, and uses the highest selection rate as the output result of the acupoint recommendation module; It further includes an expert acupoint selection result recording module, which is used to record the acupoint selection results of clinical experts for each patient with knee osteoarthritis and transmit them to the data storage module; In the acupoint recommendation module, obtaining the selection rates of experts for each acupoint within the corresponding pattern in the pattern library includes: The data storage module stores the acupoint selection data of each patient collected by the expert acupoint selection result recording module. The pattern library construction module statistically processes the acupoint selection data of each collected patient. Different groups of patients correspond to different patterns. The acupoint recommendation module statistically processes and sorts the expert acupoint selection data of different groups of patients to obtain the acupoint selection rates corresponding to different patterns, that is, the selection rates of experts for each acupoint of each patient with knee osteoarthritis in each pattern, which are used as the basis for pattern-based acupoint selection recommendations; In the infrared image pattern identification module, the identification of the patterns of newly diagnosed patients with knee osteoarthritis includes: Extracting the infrared image features according to the infrared images of newly diagnosed patients collected by the infrared image acquisition module; Performing multi-dimensional Euclidean distance calculations between the infrared image features of the newly diagnosed patients and the constructed pattern library; Sorting the Euclidean distances calculated for all the infrared image features in ascending order. The smaller the distance, the higher the matching degree, and the larger the distance, the lower the matching degree; Outputting the top five pattern identification results according to the matching degree between the new patient data and each pattern in the pattern library.

2. The infrared thermal imaging assisted acupoint selection system according to claim 1, wherein, In the pattern library construction module, the construction of the pattern library for different types of patients with knee osteoarthritis includes: Grouping the infrared image data according to the data of patients with knee osteoarthritis collected by the interrogation module; Extracting the infrared image features of each group of people for clustering analysis; wherein the infrared image features include: the maximum value, average value, and minimum value of the temperature of each acupoint in the human lower limb image; Performing supervised clustering analysis on each of the infrared image features to determine the confidence interval of each infrared image feature of each group of people, and establishing an infrared thermal image feature pattern library for each group of people.

3. The infrared thermal imaging-assisted acupoint selection system according to claim 1, wherein It further includes a patient management module, which is used for the daily maintenance of patient information, including adding, deleting, modifying, and / or querying each storage field.

4. The infrared thermal imaging-assisted acupoint selection system according to claim 1, wherein It further includes an acupuncture prescription recording module, which is used to record the acupuncture point information of clinicians for each patient visit, to statistically calculate the accuracy rate of acupoint selection results, and to adjust the acupoint recommendation coefficient according to the accuracy rate of acupoint selection results, so as to realize the feedback optimization of the acupoint recommendation module.

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