Blood fat data processing equipment and system based on traditional Chinese medicine inspection diagnosis model, and medium

Abstract

The invention discloses blood fat data processing equipment and system based on a traditional Chinese medicine inspection diagnosis model and a storage medium. The equipment comprises a memory and a processor, the memory is used for storing program instructions; the processor is used for calling a program instruction, and when the program instruction is executed, the blood fat data processing method based on the traditional Chinese medicine inspection diagnosis model is executed and comprises the steps that inspection diagnosis images of a to-be-tested subject are obtained, and the inspection diagnosis images comprise a hand diagnosis image, a forearm valley inspection diagnosis image and a face image; extracting physical signs of the inspection diagnosis image, inputting the physical signs of the inspection diagnosis image into a traditional Chinese medicine inspection diagnosis model based on CITCM-DL, analyzing correlation between the physical signs of the inspection diagnosis image and dyslipidemia, and determining an inspection diagnosis scoring standard; and performing dyslipidemia scoring on the signs of the inspection image according to the inspection scoring standard, and performing analysis to obtain an analysis result. The traditional Chinese medicine inspection diagnosis model is established in combination with inspection diagnosis scoring standards, and reference is provided for early clinical diagnosis and treatment of dyslipidemia.

Description

Technical Field

[0001] This invention relates to the field of traditional Chinese medicine intelligent technology, and in particular to a blood lipid data processing device, system, and medium based on a traditional Chinese medicine observation model. Background Technology

[0002] Dyslipidemia is a significant risk factor for cardiovascular and cerebrovascular diseases such as coronary heart disease and stroke. The key to diagnosing and treating dyslipidemia lies in early diagnosis and treatment, as well as actively controlling its risk factors. Besides blood lipid testing, traditional Chinese medicine's diagnostic method of observation, which does not require blood draws or testing equipment, has advantages in diagnosing dyslipidemia, especially in remote rural areas and pre-hospital emergency settings, where it can detect dyslipidemia earlier than blood lipid testing.

[0003] Current clinical studies on the prediction of dyslipidemia using traditional Chinese medicine observation methods mostly employ a small number of single observation methods, and there is still a lack of standardization, making it difficult to quickly provide auxiliary diagnostic suggestions. Summary of the Invention

[0004] According to one aspect of the present invention, a blood lipid data processing device and system based on a traditional Chinese medicine diagnostic model is provided, which has the advantages of being simple and inexpensive compared to blood lipid testing methods, and is better suited for early screening of abnormal blood lipids.

[0005] In a first aspect, this application provides a blood lipid data processing device based on a traditional Chinese medicine diagnostic model, the device comprising: a memory and

[0006] processor;

[0007] The memory is used to store program instructions;

[0008] The processor is used to invoke program instructions. When the program instructions are executed, they are used to perform a blood lipid data processing method based on a traditional Chinese medicine diagnostic model. The method includes the following operations:

[0009] Acquire visual images of the subject to be tested, including hand images, forearm valley images, and facial images;

[0010] The physical signs of the visual diagnosis images are extracted and input into a TCM visual diagnosis model based on CITCM-DL. The correlation between the physical signs of the visual diagnosis images and dyslipidemia is analyzed to determine the visual diagnosis scoring criteria. The dyslipidemia is scored based on the physical signs of the visual diagnosis images according to the visual diagnosis scoring criteria and analyzed to obtain the analysis results.

[0011] In some embodiments, the hand diagnosis image includes the palm area and the palm color;

[0012] The forearm valley observation image includes the forearm skin area and forearm skin texture in a relaxed state and / or a clenched fist state.

[0013] The facial image includes the eyelid area and the corneal area.

[0014] In some embodiments, the signs in the visual examination images include hand signs, forearm valley signs, and facial signs;

[0015] The hand diagnosis signs include whether there are any abnormalities in the palm area and the color of the palm, and the degree of abnormality.

[0016] The forearm valley sign includes the degree of prominence of the forearm skin area in a relaxed state and / or a clenched fist state, as well as the degree of abnormality in the forearm skin texture.

[0017] The facial signs include the presence and diameter of xanthelasma in the eyelid area; and the presence and extent of corneal arcus senilis and / or corneal arches in the corneal area.

[0018] In some implementations, the signs in the visual examination images are scored according to a scoring standard, specifically as follows:

[0019] Obtain the visual diagnosis scoring criteria, which include the hand diagnosis sign scoring criteria, the forearm valley sign scoring criteria, and the facial sign scoring criteria;

[0020] The physical signs in the visual diagnosis images are scored according to the visual diagnosis scoring criteria to obtain a comprehensive visual diagnosis score in traditional Chinese medicine.

[0021] In some implementations, the hand diagnosis sign scoring standard uses normal palm color as the standard and divides it into 4 levels according to the degree of abnormal redness of the palm color.

[0022] The forearm valley feature scoring standard uses the forearm with the highest valley abnormality score as the standard. Normal forearm skin texture is used as the benchmark. The scoring is divided into four levels based on the visibility and degree of abnormality of the forearm skin texture: normal, mild abnormality, moderate abnormality, and severe abnormality. The normal state is defined as the presence of skin texture and intermuscular depressions in the forearm valley area in a relaxed state; mild abnormality is defined as the presence of three or more intermuscular depressions in the forearm valley area when the fist is clenched; moderate abnormality is defined as the presence of one to two intermuscular depressions in the forearm valley area when the fist is clenched; and severe abnormality is defined as the absence of skin texture and intermuscular depressions in the forearm valley area in both relaxed and clenched states.

[0023] The facial assessment criteria include eyelid region scoring criteria and corneal region scoring criteria. The eyelid region scoring criteria are based on the absence of xanthelasma in the eyelid region, and are divided into four levels according to the presence or absence of xanthelasma and its diameter: Level 1 is the absence of xanthelasma in the eyelid region, Level 2 is xanthelasma with a diameter less than 5 mm, Level 3 is xanthelasma with a diameter not less than 5 mm and less than 10 mm, and Level 4 is xanthelasma with a diameter greater than 10 mm. When the scores of the left and right eyelid regions are inconsistent, the score of the eyelid region with the highest score shall prevail.

[0024] The corneal region scoring standard is based on the absence of corneal arcus senilis and / or corneal arches. The scoring is divided into four levels according to the presence and degree of visibility of corneal arcus senilis and / or corneal arches: Level 1 is the absence of corneal arcus senilis; Level 2 is the slight visibility of corneal arcus senilis and / or corneal arches; Level 3 is the visibility of corneal arcus senilis and corneal arches; and Level 4 is the obvious visibility of corneal arcus senilis and corneal arches. When the scores of the left and right corneal regions of the face are inconsistent, the score of the side with the highest score is used.

[0025] In some implementations, the analysis results include a comprehensive TCM diagnostic score of the visual images, as well as the degree of abnormality and TCM diagnostic results for dyslipidemia.

[0026] If the comprehensive score of TCM observation diagnosis is 0, the degree of abnormality is no abnormality; if the comprehensive score of TCM observation diagnosis is 0.5 to 1, the degree of abnormality is mild; if the comprehensive score of TCM observation diagnosis is 1.5 to 3.5, the degree of abnormality is moderate; if the comprehensive score of TCM observation diagnosis is 4 to 6, the degree of abnormality is severe.

[0027] The results of TCM diagnostic examination for dyslipidemia include negative or positive. A TCM diagnostic score of 0-1 indicates a negative result for dyslipidemia, while a TCM diagnostic score of 1.5-6 indicates a positive result for dyslipidemia.

[0028] In some implementations, constructing a CITCM-DL-based TCM diagnostic model includes the following steps:

[0029] A training dataset was constructed based on the images obtained through traditional Chinese medicine observation, which included a positive group and a negative group. The positive and negative groups were scored, and the correlation between the signs in the images and the results of traditional Chinese medicine observation and dyslipidemia, different types of dyslipidemia, and risk factors for dyslipidemia was analyzed.

[0030] Determine the scoring criteria for visual diagnosis based on correlation;

[0031] By scoring the physical signs in the visual diagnosis images using the visual diagnosis scoring criteria, a traditional Chinese medicine visual diagnosis model based on CITCM-DL was obtained.

[0032] In some implementations, the positive and negative groups are scored, and the correlation between the TCM comprehensive diagnostic results of the visual signs and dyslipidemia is analyzed, including:

[0033] Spearman correlation test showed that dyslipidemia was positively correlated with positive hand diagnosis signs (redness and rosiness), positive forearm metaplasia, corneal arcus / corneal auricle, and TCM comprehensive observation score.

[0034] The variables were analyzed using a multivariate unconditional logistic stepwise regression method. The dependent variable was the presence of dyslipidemia, and the independent variables included gender, age, BMI, waist circumference, hypertension, hyperglycemia, hyperuricemia, family history of cardiovascular disease, smoking, alcohol consumption, lack of aerobic exercise, high psychological stress, abnormal redness of the palms, abnormal metaplasia of the forearm, xanthoma of the eyelids, arcus senilis of the cornea, and dyslipidemia as diagnosed by traditional Chinese medicine observation.

[0035] A forward stepwise method was used to screen variables, remove relevant variables, and determine the independent variables related to dyslipidemia. The independent variables included hyperuricemia, abnormally rosy palms, xanthelasma palpebrarum, and arcus senilis of the cornea.

[0036] Secondly, this application provides a blood lipid data processing system based on a traditional Chinese medicine diagnostic model, comprising:

[0037] The image acquisition module acquires visual images of the subject under test, including hand images, forearm valley visual images, and facial images.

[0038] The data processing module extracts the physical signs from the visual diagnosis images, inputs the physical signs from the visual diagnosis images into a TCM visual diagnosis model based on CITCM-DL, analyzes the correlation between the physical signs from the visual diagnosis images and dyslipidemia, and determines the visual diagnosis scoring criteria; according to the visual diagnosis scoring criteria, the physical signs from the visual diagnosis images are scored for dyslipidemia and analyzed to obtain the analysis results.

[0039] Thirdly, this application provides a computer-readable storage medium, comprising:

[0040] Memory containing executable program code;

[0041] A processor coupled to the memory;

[0042] The processor calls the executable program code stored in the memory to perform an operation of a blood lipid data processing method based on a traditional Chinese medicine diagnostic model. The method includes the following operations:

[0043] Acquire visual images of the subject to be tested, including hand images, forearm valley images, and facial images;

[0044] The physical signs of the visual diagnosis images are extracted and input into a TCM visual diagnosis model based on CITCM-DL. The correlation between the physical signs of the visual diagnosis images and dyslipidemia is analyzed to determine the visual diagnosis scoring criteria. The dyslipidemia is scored based on the physical signs of the visual diagnosis images according to the visual diagnosis scoring criteria and analyzed to obtain the analysis results.

[0045] Beneficial effects:

[0046] This application provides a blood lipid data processing device, system, and medium based on a traditional Chinese medicine (TCM) diagnostic model. It utilizes the TCM diagnostic model to predict dyslipidemia in its early stages, analyzes the correlation between the CITCM-DL method and abnormal forearm metaplasia with dyslipidemia, and clarifies that the CITCM-DL method, abnormal palmar rosiness, xanthelasma of the eyelids, and corneal arcus senilis are all positively correlated with dyslipidemia. This facilitates early screening for dyslipidemia, fills some gaps in the TCM diagnostic theory of dyslipidemia, and provides a reference for the early diagnosis of dyslipidemia using TCM diagnostic methods. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating a blood lipid data processing device based on a traditional Chinese medicine diagnostic model provided by the present invention. Detailed Implementation

[0048] The present invention will now be described in further detail with reference to the accompanying drawings.

[0049] like Figure 1 As shown, this application provides a blood lipid data processing device based on a traditional Chinese medicine observation model. Based on the traditional Chinese medicine observation model, the device provides a reference for the early clinical diagnosis and treatment of dyslipidemia through observation images.

[0050] The device includes a memory and a processor;

[0051] The memory is used to store program instructions;

[0052] The processor is used to invoke program instructions. When the program instructions are executed, they are used to perform a blood lipid data processing method based on a traditional Chinese medicine diagnostic model. The method includes the following operations:

[0053] Step S1: Obtain visual images of the subject to be tested, including hand images, forearm valley images, and facial images.

[0054] Specifically, the hand diagnosis image includes the palm area and palm color; the forearm valley diagnosis image includes the forearm skin area and forearm skin texture; the facial image includes the eyelid area and corneal area. The signs in the diagnosis images include hand diagnosis signs, forearm valley signs, and facial signs; the hand diagnosis signs include whether there are abnormalities in the palm area and palm color, and the degree of abnormality; the forearm valley signs include the degree of prominence of the forearm skin area and the degree of abnormality in the forearm skin texture in a relaxed state and / or clenched fist state; the facial signs include whether there are xanthelasma in the eyelid area and the diameter of the xanthelasma; and the presence and degree of prominence of corneal arcus senilis and / or corneal arches in the corneal area.

[0055] Step S2: Extract the physical signs from the visual diagnosis images, input the physical signs from the visual diagnosis images into the TCM visual diagnosis model based on CITCM-DL, analyze the correlation between the physical signs from the visual diagnosis images and dyslipidemia, and determine the visual diagnosis scoring criteria; score the physical signs from the visual diagnosis images for dyslipidemia according to the visual diagnosis scoring criteria and analyze the results.

[0056] The steps involved in constructing a traditional Chinese medicine diagnostic model based on CITCM-DL are as follows:

[0057] Step S21: Construct a training dataset based on the visual diagnosis images, the training dataset including a positive group and a negative group; score the positive group and the negative group, and analyze the correlation between the TCM comprehensive visual diagnosis results of the physical signs in the visual diagnosis images and dyslipidemia, different types of dyslipidemia, and risk factors of dyslipidemia;

[0058] Step S22: Determine the scoring criteria for visual diagnosis based on correlation;

[0059] Step S23: Score the physical signs in the visual diagnosis images using the visual diagnosis scoring criteria to obtain the TCM visual diagnosis model.

[0060] Specifically, based on the observation images of 336 subjects from previous research, including 308 subjects with dyslipidemia and 28 subjects without dyslipidemia, a training dataset for a traditional Chinese medicine observation model was constructed. The observation images and signs of subjects with dyslipidemia were designated as the positive group, while those of subjects without dyslipidemia were designated as the negative group. The training dataset was manually observed, and multiple signs in the observation images were labeled using professional medical knowledge. The collected observation information on the hands, forearm valleys, and face was scored to analyze the relationship between comprehensive traditional Chinese medicine observation and dyslipidemia.

[0061] The physical signs in the visual examination include hand signs, forearm valley signs, and facial signs; the hand signs include whether there are any abnormalities in the palm area and palm color, and the degree of abnormality; the forearm valley signs include the degree of prominence of the forearm skin area and the degree of abnormality in the forearm skin texture in a relaxed state and / or a clenched fist state; the facial signs include whether there are xanthelasma in the eyelid area and the diameter of the xanthelasma; and the presence and degree of prominence of corneal arcus and / or corneal arch in the corneal area.

[0062] Based on the physical signs described above in the positive and negative groups, the collected information on hand observation, forearm palpable vein observation, and facial observation was scored to analyze the relationship between comprehensive TCM observation and dyslipidemia. The scoring criteria for these physical signs are shown in Table 1.

[0063] Table 1. Scoring criteria and significance of TCM diagnostic signs of dyslipidemia.

[0064]

[0065]

[0066] Note: Among the above-mentioned signs of visual examination, a score of 0 for hand, forearm valley, and face examination indicates no abnormality; 0.5–1 indicates mild abnormality; 1.5–3.5 indicates moderate abnormality; and 4–6 indicates severe abnormality. A score of 0–1 indicates negative CITCM-DL (Critical Iodine Technology Clinical Examination-Diagnosis-Dyslipidemia); and a score of 1.5–6 indicates positive CITCM-DL (Critical Iodine Technology Clinical Examination-Dyslipidemia ...

[0067] Specifically, the positive and negative groups were scored, and the correlation between the TCM comprehensive observation results of the visual examination images and dyslipidemia was analyzed, including:

[0068] Spearman correlation test showed that dyslipidemia was positively correlated with positive hand diagnosis signs (redness and rosiness), positive forearm metaplasia, corneal arcus / corneal arch, and the comprehensive TCM observation score.

[0069] The variables were analyzed using a multivariate unconditional logistic stepwise regression method. The dependent variable was the presence of dyslipidemia, and the independent variables included gender, age, BMI, waist circumference, hypertension, hyperglycemia, hyperuricemia, family history of cardiovascular disease, smoking, alcohol consumption, lack of aerobic exercise, high psychological stress, abnormal redness of the palms, abnormal metaplasia of the forearm, xanthoma of the eyelids, arcus senilis of the cornea, and dyslipidemia as diagnosed by traditional Chinese medicine observation.

[0070] A forward stepwise method was used to screen variables, remove relevant variables, and determine the independent variables related to dyslipidemia. The independent variables included hyperuricemia, abnormally rosy palms, xanthelasma palpebrarum, and arcus senilis of the cornea.

[0071] In this application, SPSS 18.0 software was used for statistical analysis. Quantitative data were expressed as mean ± standard deviation (x ± s) or median (interquartile range), and categorical data were expressed as frequency or percentage. For quantitative data conforming to normal distribution and homogeneity of variance, t-tests were used. For quantitative data not conforming to normal distribution or heterogeneity of variance, rank-sum tests were used. Chi-square tests or Fisher's exact tests were used for comparisons of ordinal categorical data between groups. Spearman correlation analysis was used for the correlation analysis between traditional Chinese medicine observation and dyslipidemia. Unconditional stepwise logistic regression analysis was used for multivariate analysis of risk factors for dyslipidemia. A two-tailed significance level of α = 0.05 was used, with P < 0.05 considered statistically significant.

[0072] The specific analysis process is as follows:

[0073] 1. Comparison of TCM diagnostic results between subjects with and without dyslipidemia

[0074] Of the 336 subjects in the training dataset, 28 were not dyslipidemia and 308 were dyslipidemia. In both groups, 45.8% were female; 10.1% were aged 40-54 years, 26.5% were aged 55-64 years, and 63.4% were ≥65 years. 35.4% were overweight, 11.6% were obese, 67.9% had central obesity, and 17.6% had pre-central obesity. The percentages of subjects testing positive for abnormally red palms (89.9%), abnormal metaplasia of the forearm (66.4%), xanthoma of the eyelids (72.0%), corneal arcus senilis (74.1%), and traditional Chinese medicine diagnostic methods (87.5%) were also positive. Regarding the comparison of positive results for abnormally red palms, abnormal metaplasia of the forearm, xanthelasma of the eyelids, arcus senilis of the cornea, and positive results from comprehensive TCM observation, the positive rates in the dyslipidemia group were 92.2%, 69.8%, 78.2%, 79.2%, and 91.6%, respectively, while those in the non-dyslipidemia group were 64.3%, 28.6%, 3.6%, 17.9%, and 42.9%, respectively. The differences between the two groups were statistically significant using chi-square or continuity chi-square tests (P < 0.05). The distribution of scores for abnormally red palms, abnormal metaplasia of the forearm, xanthelasma of the eyelids, arcus senilis of the cornea, and positive results from comprehensive TCM observation between the two groups was also statistically significant using Fisher's exact test (P < 0.05), as detailed in Table 2.

[0075] Table 2. Comparison of TCM diagnostic results between the negative and positive groups of subjects with and without dyslipidemia in the training data sets.

[0076]

[0077]

[0078]

[0079] Note: a) The comparison of the two groups of abnormal redness and positive results in hand diagnosis and abnormal blood lipids in TCM comprehensive observation diagnosis was performed using the continuity-corrected chi-square test.

[0080] b. Fisher's exact test was used to compare the scores of abnormal redness of the palms, abnormal metaplasia of the forearms, xanthoma of the eyelids, arcus senilis of the cornea, and abnormal blood lipids in the two groups based on the comprehensive TCM observation diagnosis.

[0081] 2. Correlation analysis between traditional Chinese medicine observation and different types of dyslipidemia

[0082] In the training dataset of 336 subjects, Spearman correlation test showed that dyslipidemia was significantly and weakly positively correlated with abnormal redness of the palms and scores, abnormal metaplasia of the forearm and scores, and corneal arcus senilis scores (r = 0.256, 0.124, 0.241, 0.174 and 0.298, respectively, P < 0.05). Dyslipidemia was significantly and weakly positively correlated with xanthelasma of the eyelids and scores, corneal arcus senilis, and positive results and scores of traditional Chinese medicine comprehensive observation diagnosis (r = 0.460, 0.383, 0.387, 0.407 and 0.354, respectively, P < 0.05). Among the 336 subjects, no statistically significant correlation was found between hypercholesterolemia in the past two weeks and positive results and scores on TCM diagnostic methods. However, a very weak positive correlation was found between hypertriglyceridemia in the past two weeks and positive results and scores on abnormal metaplasia of the forearm (r = 0.183 and 0.209, respectively, P < 0.05). Similarly, a very weak positive correlation was found between mixed hyperlipidemia in the past two weeks and positive results and scores on abnormal metaplasia of the forearm (r = 0.183 and 0.209, respectively, P < 0.05). The correlation coefficients were 0.124 and 0.163 (P < 0.05). In the past two weeks, low high-density lipoprotein cholesterol was significantly weakly positively correlated with the following abnormalities and scores: forearm metaplasia, xanthelasma of the eyelids, arcus senilis of the cornea, and TCM comprehensive inspection and scores (r = 0.175, 0.149, 0.186, 0.131, 0.115, 0.125 and 0.125, respectively, P < 0.05). See Table 3 for details.

[0083] Table 3. Correlation analysis between TCM observation and different types of dyslipidemia.

[0084]

[0085] Note: a Dyslipidemia is defined as a condition in which a subject has a history of dyslipidemia, whether or not they have taken lipid-lowering drugs, and whose blood lipid levels have been abnormal or normal in the past two weeks.

[0086] b Abnormal blood lipids in the past two weeks refers to the result of blood lipid tests in the past two weeks, whether or not the subject has a history of abnormal blood lipids.

[0087] 3. Predictive analysis of risk factors for dyslipidemia

[0088] This study employed multivariate unconditional logistic stepwise regression (forward: LR) with dyslipidemia as the dependent variable (0 = none, 1 = yes). Independent variables included gender (0 = female, 1 = male), age (0: 40–54 years, 1: 55–64 years, 2: ≥65 years), BMI (0: <24.0, 1: 24.0–27.9, 2: ≥28.0), waist circumference (0: normal waist circumference, 1: pre-central obesity, 2: central obesity), hypertension (0: none, 1: yes), hyperglycemia (0: none, 1: yes), hyperuricemia (0: none, 1: yes), and family history of cardiovascular disease. The following were considered positive for various diseases: hyperuricemia (0: no, 1: yes), smoking (0: no, 1: yes), alcohol consumption (0: no, 1: yes), lack of aerobic exercise (0: no, 1: yes), high psychological stress (0: no, 1: yes), abnormally red palms (0: no, 1: yes), abnormal metaplasia of the forearm (0: no, 1: yes), xanthelasma of the eyelids (0: no, 1: yes), arcus senilis of the cornea (0: no, 1: yes), and dyslipidemia (0: no, 1: yes). This study used forward stepwise (likelihood ratio) variable screening. After removing relevant variables, the statistically significant independent variables remaining in the equation were hyperuricemia, abnormally red palms, xanthelasma of the eyelids, and arcus senilis of the cornea, with odds ratios (ORs) of 6.632, 6.076, 58.757, and 13.459, respectively (P < 0.05). The analysis results suggest that hyperuricemia, abnormally rosy palms, xanthoma of the eyelids, and arcus senilis of the cornea may be independent risk factors for predicting the occurrence of dyslipidemia, as detailed in Table 4.

[0089] Table 4. Logistic Regression Analysis of Risk Factors for Dyslipidemia

[0090]

[0091] The results show that a self-developed TCM diagnostic model for early prediction of dyslipidemia has been innovatively established for the first time. Based on the patient's hand, forearm, and facial (eyelid xanthelasma and corneal arcus) diagnostic information, combined with the self-developed scoring criteria, a comprehensive TCM diagnostic model for dyslipidemia has been developed. The correlation between TCM diagnostic model and dyslipidemia has been explored, providing a reference for the early diagnosis of dyslipidemia through TCM diagnostic model. It has high practical value in clinical practice.

[0092] After acquiring the visual diagnosis image, the physical signs in the image are extracted and input into the TCM visual diagnosis model. The TCM visual diagnosis model obtains the visual diagnosis scoring criteria, scores the physical signs in the visual diagnosis image according to the criteria, and obtains a comprehensive TCM visual diagnosis score; the comprehensive TCM visual diagnosis score is then analyzed to obtain the analysis results.

[0093] The analysis results include the comprehensive TCM diagnostic score of the visual image, the degree of abnormality, and the TCM diagnostic result for dyslipidemia. A comprehensive TCM diagnostic score of 0 indicates no abnormality; a score of 0.5–1 indicates mild abnormality; a score of 1.5–3.5 indicates moderate abnormality; and a score of 4–6 indicates severe abnormality. The TCM diagnostic result for dyslipidemia can be negative or positive. A score of 0–1 indicates a negative result for TCM diagnostic analysis of dyslipidemia, while a score of 1.5–6 indicates a positive result.

[0094] This application also provides a system, including:

[0095] The image acquisition module acquires visual images of the subject under test, including hand images, forearm valley visual images, and facial images.

[0096] The data processing module extracts the physical signs from the visual diagnosis images, inputs the physical signs from the visual diagnosis images into the traditional Chinese medicine visual diagnosis model, analyzes the correlation between the physical signs from the visual diagnosis images and dyslipidemia, and determines the visual diagnosis scoring criteria; according to the visual diagnosis scoring criteria, the physical signs from the visual diagnosis images are scored for dyslipidemia and analyzed to obtain the analysis results.

[0097] This application provides a blood lipid data processing device and system based on a traditional Chinese medicine (TCM) diagnostic model. The model enables early prediction of dyslipidemia, analyzes the correlation between the CITCM-DL method and abnormal forearm metaplasia with dyslipidemia, and clarifies that the CITCM-DL method, abnormal palmar rosiness, eyelid xanthelasma, and corneal arcus senilis are all positively correlated with dyslipidemia. This facilitates early screening for dyslipidemia, fills some gaps in the TCM diagnostic theory of dyslipidemia, and provides a reference for the early diagnosis of dyslipidemia using TCM diagnostic methods.

[0098] Based on the same inventive concept, this application also provides a computer-readable storage medium, including a memory storing executable program code;

[0099] A processor coupled to the memory;

[0100] The processor calls the executable program code stored in the memory to execute a blood lipid data processing method based on a traditional Chinese medicine diagnostic model, as described above.

[0101] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute a described method for processing blood lipid data based on a traditional Chinese medicine diagnostic model.

[0102] The embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules. They may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0103] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0104] Finally, it should be noted that the disclosed content of the embodiments of the present invention is only a preferred embodiment of the present invention and is only used to illustrate the technical solutions of the present invention, and is not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A blood lipid data processing device based on a traditional Chinese medicine diagnostic model, characterized in that, The device includes: a memory and processor; The memory is used to store program instructions; The processor is used to invoke program instructions. When the program instructions are executed, they are used to perform a blood lipid data processing method based on a traditional Chinese medicine diagnostic model. The method includes the following operations: Acquire visual images of the subject to be tested, including hand images, forearm valley images, and facial images; The physical signs of the visual diagnosis images are extracted and input into a TCM visual diagnosis model based on CITCM-DL. The correlation between the physical signs of the visual diagnosis images and dyslipidemia is analyzed to determine the visual diagnosis scoring criteria. The dyslipidemia is scored based on the physical signs of the visual diagnosis images according to the visual diagnosis scoring criteria and analyzed to obtain the analysis results.

2. The blood lipid data processing device based on the traditional Chinese medicine diagnostic model according to claim 1, characterized in that, The hand diagnosis image includes the palm area and the palm color; The forearm valley observation image includes the forearm skin area and forearm skin texture in a relaxed state and / or a clenched fist state. The facial image includes the eyelid area and the corneal area.

3. The blood lipid data processing device based on the traditional Chinese medicine diagnostic model according to claim 2, characterized in that, The physical signs described in the visual diagnosis images include hand signs, forearm valley signs, and facial signs; The hand diagnosis signs include whether there are any abnormalities in the palm area and the color of the palm, and the degree of abnormality. The forearm valley sign includes the degree of prominence of the forearm skin area in a relaxed state and / or a clenched fist state, as well as the degree of abnormality in the forearm skin texture. The facial signs include the presence and diameter of xanthelasma in the eyelid area; and the presence and extent of corneal arcus senilis and / or corneal arches in the corneal area.

4. The blood lipid data processing device based on the traditional Chinese medicine diagnostic model according to claim 2, characterized in that, The signs in the visual examination images are scored according to the scoring criteria, specifically as follows: Obtain the visual diagnosis scoring criteria, which include the hand diagnosis sign scoring criteria, the forearm valley sign scoring criteria, and the facial sign scoring criteria; The physical signs in the visual diagnosis images are scored according to the visual diagnosis scoring criteria to obtain a comprehensive visual diagnosis score in traditional Chinese medicine.

5. The blood lipid data processing device based on the traditional Chinese medicine diagnostic model according to claim 4, characterized in that, The hand diagnosis sign scoring standard uses normal palm color as the standard and divides it into 4 levels according to the degree of abnormal redness of the palm color. The forearm valley sign scoring standard uses the forearm with the highest valley transformation abnormality score and normal forearm skin texture as the standard. The score is divided into 4 levels according to the obviousness and abnormality of the forearm skin texture: normal state, mild abnormality, moderate abnormality and severe abnormality. The normal state is that the forearm valley area has skin texture and intermuscular depressions in a relaxed state; the mild abnormality is that there are 3 or more intermuscular depressions in the forearm valley area in a clenched fist state; the moderate abnormality is that there are 1 to 2 intermuscular depressions in the forearm valley area in a clenched fist state; the severe abnormality is that the forearm valley area does not show skin texture and intermuscular depressions in either a relaxed or clenched fist state. The facial assessment criteria include eyelid region scoring criteria and corneal region scoring criteria. The eyelid region scoring criteria are based on the absence of xanthelasma in the eyelid region, and are divided into four levels according to the presence or absence of xanthelasma and its diameter: Level 1 is the absence of xanthelasma in the eyelid region, Level 2 is xanthelasma with a diameter less than 5 mm, Level 3 is xanthelasma with a diameter not less than 5 mm and less than 10 mm, and Level 4 is xanthelasma with a diameter greater than 10 mm. When the scores of the left and right eyelid regions are inconsistent, the score of the eyelid region with the highest score shall prevail. The corneal region scoring standard is based on the absence of corneal arcus senilis and / or corneal arches. The scoring is divided into four levels according to the presence and degree of visibility of corneal arcus senilis and / or corneal arches: Level 1 is the absence of corneal arcus senilis; Level 2 is the slight visibility of corneal arcus senilis and / or corneal arches; Level 3 is the visibility of corneal arcus senilis and corneal arches; and Level 4 is the obvious visibility of corneal arcus senilis and corneal arches. When the scores of the left and right corneal regions of the face are inconsistent, the score of the side with the highest score is used.

6. The blood lipid data processing device based on the traditional Chinese medicine diagnostic model according to claim 5, characterized in that, The analysis results include the comprehensive score of TCM visual diagnosis of the visual diagnosis images, as well as the degree of abnormality and the TCM visual diagnosis results of dyslipidemia. If the comprehensive score of TCM observation diagnosis is 0, the degree of abnormality is no abnormality; if the comprehensive score of TCM observation diagnosis is 0.5 to 1, the degree of abnormality is mild; if the comprehensive score of TCM observation diagnosis is 1.5 to 3.5, the degree of abnormality is moderate; if the comprehensive score of TCM observation diagnosis is 4 to 6, the degree of abnormality is severe. The results of TCM diagnostic examination for dyslipidemia include negative or positive. A TCM diagnostic score of 0-1 indicates a negative result for dyslipidemia, while a TCM diagnostic score of 1.5-6 indicates a positive result for dyslipidemia.

7. The blood lipid data processing device based on the traditional Chinese medicine diagnostic model according to any one of claims 1-3, characterized in that, The steps involved in constructing a traditional Chinese medicine diagnostic model based on CITCM-DL are as follows: A training dataset was constructed based on the images obtained through traditional Chinese medicine observation, which included a positive group and a negative group. The positive and negative groups were scored, and the correlation between the signs in the images and the results of traditional Chinese medicine observation and dyslipidemia, different types of dyslipidemia, and risk factors for dyslipidemia was analyzed. Determine the scoring criteria for visual diagnosis based on correlation; By scoring the physical signs in the visual diagnosis images using the visual diagnosis scoring criteria, a traditional Chinese medicine visual diagnosis model based on CITCM-DL was obtained.

8. The blood lipid data processing device based on the traditional Chinese medicine observation model according to claim 7, characterized in that, The positive and negative groups were scored, and the correlation between the TCM comprehensive observation results of the physical signs in the visual examination images and dyslipidemia was analyzed, including: Spearman correlation test showed that dyslipidemia was positively correlated with positive hand diagnosis signs (redness and rosiness), positive forearm metaplasia, corneal arcus / corneal auricle, and TCM comprehensive observation score. The variables were analyzed using a multivariate unconditional logistic stepwise regression method. The dependent variable was the presence of dyslipidemia, and the independent variables included gender, age, BMI, waist circumference, hypertension, hyperglycemia, hyperuricemia, family history of cardiovascular disease, smoking, alcohol consumption, lack of aerobic exercise, high psychological stress, abnormal redness of the palms, abnormal metaplasia of the forearm, xanthoma of the eyelids, arcus senilis of the cornea, and dyslipidemia as diagnosed by traditional Chinese medicine observation. A forward stepwise method was used to screen variables, remove relevant variables, and determine the independent variables related to dyslipidemia. The independent variables included hyperuricemia, abnormally rosy palms, xanthelasma palpebrarum, and arcus senilis of the cornea.

9. A blood lipid data processing system based on a traditional Chinese medicine diagnostic model, characterized in that, include: The image acquisition module acquires visual images of the subject under test, including hand images, forearm valley visual images, and facial images. The data processing module extracts the physical signs from the visual diagnosis images, inputs the physical signs from the visual diagnosis images into a TCM visual diagnosis model based on CITCM-DL, analyzes the correlation between the physical signs from the visual diagnosis images and dyslipidemia, and determines the visual diagnosis scoring criteria; according to the visual diagnosis scoring criteria, the physical signs from the visual diagnosis images are scored for dyslipidemia and analyzed to obtain the analysis results.

10. A computer-readable storage medium, characterized in that, include: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to perform an operation of a blood lipid data processing method based on a traditional Chinese medicine diagnostic model. The method includes the following operations: Acquire visual images of the subject to be tested, including hand images, forearm valley images, and facial images; The physical signs of the visual diagnosis images are extracted and input into a TCM visual diagnosis model based on CITCM-DL. The correlation between the physical signs of the visual diagnosis images and dyslipidemia is analyzed to determine the visual diagnosis scoring criteria. The dyslipidemia is scored based on the physical signs of the visual diagnosis images according to the visual diagnosis scoring criteria and analyzed to obtain the analysis results.

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