315 results about "Aids diagnostics" patented technology

The invention discloses a traditional Chinese medicine rehabilitation diagnosis system based on a multi-modal knowledge graph and a large language model. The invention provides a traditional Chinese medicine rehabilitation diagnosis system based on a multi-modal knowledge graph and a large language model. The traditional Chinese medicine rehabilitation diagnosis system can be used for intelligent inquiry or clinical auxiliary diagnosis scenes. The invention provides a finer multi-modal data fusion and knowledge graph retrieval method aiming at the problem that multi-modal data in traditional Chinese medicine rehabilitation clinic is difficult to fully mine and utilize. Specifically, the method comprises the following steps: firstly, performing targeted preprocessing according to the characteristics of a tongue coating image and a pulse condition signal; then, the tongue coating and the pulse condition are fused on the feature level and aligned with the embedding of the knowledge graph. The embedded vectors are further used for retrieving related entities in a knowledge graph, so that more comprehensive and fine-grained medical knowledge related to tongue coating and pulse conditions is obtained, and the interpretability and reasoning ability of the model are enhanced.

The invention relates to a chest pain classification method and system based on multi-modal data fusion and a deep learning model, and belongs to the technical field of intelligent medical auxiliary diagnosis. The method comprises the following steps: firstly, carrying out heart region segmentation and pathological feature enhancement preprocessing on a chest radiograph image, extracting anatomical features by adopting a multi-scale visual model which is optimized and improved aiming at the chest radiograph, meanwhile, carrying out multi-lead time sequence calibration and ST-segment waveform marking on an electrocardiogram signal, and capturing electrophysiological time sequence features through a multi-scale one-dimensional convolutional network; then, a dynamic gating hybrid expert system is constructed, an anatomy expert, an electrophysiology expert, a multi-modal association expert and a critical value expert process specific modal features respectively, and a gating network dynamically calculates expert weights based on real-time vital signs and pathological features; according to the method, by integrating multi-modal data and combining a deep learning technology, rapid and accurate chest pain classification support and report generation can be provided for clinicians, and the method has a wide application prospect.

The invention belongs to the technical field of biological medicines, and particularly relates to application of a macrophage Angulin-1 gene and / or an Angulin-1 protein in preparation of medicines and diagnostic products for preventing and / or treating atherosclerosis and related diseases. Research finds that plaque and necrotic core areas of an atherosclerosis animal model are remarkably increased due to macrophage Angulin-1 gene knockout, and disease development is promoted; and the recovery of the expression level of Angulin-1 significantly inhibits the development of lesion. Cell experiments show that Angulin-1 reduces intake of oxidized low-density lipoprotein by down-regulating expression of macrophage LOX-1, so that formation of foam cells is inhibited. Therefore, the macrophage Angulin-1 can be used as an atherosclerosis drug target, a gene therapy target gene and an auxiliary diagnosis marker, and a new theoretical basis and an intervention strategy are provided for prevention and treatment of the disease.

The present invention relates to the field of disease prediction, and particularly to a cognitive impairment prediction system based on synchronously acquired electroencephalogram-electrogastrography signals, comprising: a data acquisition module for acquiring data, where the data includes electroencephalogram data and electrogastrogram data; a first analysis module for obtaining the average electrode inconsistency index of a first channel and a second channel based on a first model according to time series data; and a prediction module for comparing the average electrode inconsistency index of a subject with a preset evaluation threshold so as to predict patients with cognitive impairment. The system provided by the present invention can more accurately and efficiently distinguish patients with mild cognitive impairment from healthy people, and can be used as an auxiliary diagnostic tool for primary screening of mild cognitive impairment in a large population (such as communities, physical examination centers).

The invention discloses a biomarker for auxiliary diagnosis of progressive suprakaryotic paralysis and application thereof, and relates to the technical field of biomedicine. The biomarker is a neuron-derived extracellular vesicle carrying a Tau deformable protein; the Tau deformation protein is 4R Tau or pTau181, and the Tau deformation protein is a protein with the molecular weight of 4R The invention develops a biomarker capable of assisting in diagnosing progressive suprakaryotic paralysis, and the biomarker can be used for effectively identifying a progressive suprakaryotic paralysis (PSP) patient, a Parkinson's disease (PD) patient and a healthy person (HC). Meanwhile, a progressive supranuclear paralysis diagnosis model is also constructed, the sensitivity of the multivariable logistic regression model for distinguishing PSP and HC is 95.7%, the specificity is 90.0%, and the AUC is 0.974; the sensitivity of the multivariable logistic regression model for distinguishing PSP and PD is 96.3%, the specificity is 90.0%, and the AUC is 0.968.

The invention provides an antigen epitope peptide related to connexin and application of the antigen epitope peptide. The connexin antigen epitope peptide disclosed by the invention is selected from (1) a polypeptide with an amino acid sequence as shown in SEQ ID NO: 14; and (2) a polypeptide which is derived from (1) by substituting, deleting or adding 1-2 amino acids in the amino acid sequence of SEQ ID NO: 14 and retains the binding capacity with a connexin antibody. According to the present invention, the new connexin antigenic peptide fragment sequence is identified for the first time, such that the existing autoantigen epitope map is expanded, and more importantly, the molecular basis is provided for the development of the clinical detection method with high diagnosis sensitivity and high specificity, such that the pathological mechanism of MG can be further improved, and the application prospect is broad. And new auxiliary diagnosis means and treatment targets are provided for antibody negative MG patients.

The invention discloses a biomarker related to diabetic neuralgia and application of the biomarker. The biomarker is IGFBPs or CD324. According to the application, a DNP (Diabetic Neuralgia) rat model is constructed, the DIA and DDA combined technology is adopted, DNP model rats are subjected to serum proteome detection, and through analysis, screening and verification, compared with normal group rats, the expression level of IGFBPs or CD324 in serum of the DNP model rats has significant difference; according to the method, IGFBPs or CD324 is taken as a biomarker, a working characteristic curve (ROC) of a subject is drawn at the same time, and IGFBPs or CD324 is evaluated according to area under the curve (AUC) to have excellent diagnostic efficiency when applied to diabetic neuralgia as the biomarker, so that a new biomarker can be provided for auxiliary diagnosis of diabetic neuralgia, and clinical diagnosis of diabetic neuralgia is more convenient and accurate.

The invention discloses application of FN1 as a biomarker in diagnosis or auxiliary diagnosis of nasopharyngeal carcinoma. By detecting the serum FN1 level, healthy subjects and advanced nasopharyngeal carcinoma patients can be distinguished, early nasopharyngeal carcinoma patients and advanced nasopharyngeal carcinoma patients can be distinguished, VCA-IgA negative early nasopharyngeal carcinoma patients and advanced nasopharyngeal carcinoma patients can be distinguished, and VCA-IgA positive early nasopharyngeal carcinoma patients and advanced nasopharyngeal carcinoma patients can be distinguished. According to the present invention, the EBV DNA-negative early nasopharyngeal carcinoma patient and the EBV DNA-negative late nasopharyngeal carcinoma patient can be distinguished, the EBV DNA-positive early nasopharyngeal carcinoma patient and the EBV DNA-positive late nasopharyngeal carcinoma patient can be distinguished, the diagnosis sensitivity is high, the marker detection is convenient, rapid and non-invasive, and the good clinical application and promotion value can be provided.

The invention discloses an abnormal heart beat detection method and system based on a magnetic cardiogram, and relates to the technical field of medical data processing, and the method comprises the steps: carrying out the preprocessing of original magnetic cardiogram data; magnetic cardiogram features are extracted based on discrete wavelet transform, model parameters are adjusted, and an abnormal heart beat detection model is obtained; and judging whether abnormal heart beat exists or not and the position. According to the method, the problem that the magnetocardiogram data lacks a large number of heart beat level labeling samples is effectively solved, dependence on labeling data is reduced, automatic, efficient and accurate detection of abnormal heart beats is achieved, the good clinical application prospect is achieved, and powerful support can be provided for early screening, auxiliary diagnosis and risk assessment of arrhythmia.

The invention provides application of a substance for detecting the ratio of lymphocyte subgroups in preparation of a reagent or a kit for diagnosis or auxiliary diagnosis of Graves disease, and belongs to the technical field of preparation of disease diagnosis reagents. A research shows that compared with a healthy sample, the proportion of B cells in a lymphocyte subpopulation of a to-be-detected sample is remarkably increased, the proportion of NK cells is remarkably reduced, the proportion of activated T cells is remarkably reduced, the proportion of memory T cells is remarkably reduced, the proportion of CD4 + memory T cells is remarkably reduced, the proportion of Th1 cells is remarkably reduced, and the proportion of Tc1 cells is remarkably reduced, so that the to-be-detected sample is a GD patient; the substance for detecting the ratio of lymphocyte subgroups is high in sensitivity and specificity when being used for diagnosis or auxiliary diagnosis of Graves disease.

The invention discloses an auxiliary diagnosis system for renal transplantation rejection and typing thereof based on a peripheral blood immune cell map. Comprising a peripheral blood single cell acquisition unit, a mass spectrum flow cytometry unit, an original data preprocessing unit, a data conversion and normalization processing unit, a peripheral blood immune cell grouping acquisition unit, a peripheral blood immune cell map construction unit, a feature vector acquisition unit, a rejection prediction unit and a rejection typing prediction unit. According to the invention, a kidney transplantation two-stage diagnosis architecture is created for the first time; rejection detection and typing identification are realized through classification of an independently trained random forest binary classifier, and different TCMR / ABMR immune mechanisms are accurately matched; the traditional biopsy limitation is broken through, an auxiliary diagnosis scheme with high precision, low damage, moderate cost and typing identification capability is provided for the kidney transplantation rejection, and the method has important significance on timely discovery of the rejection of a kidney transplantation patient and improvement of prognosis.

The invention relates to the field of disease prediction, in particular to a cognitive impairment prediction system based on synchronously acquired electroencephalogram-stomach electrical signals, comprising: a data acquisition module for acquiring data including electroencephalogram data and stomach and intestine electrical data; the first analysis module is used for obtaining an average electrode inconsistency index of the first channel and the second channel based on a first model according to the time sequence data; and the prediction module is used for comparing the average electrode inconsistency index of the subject with a preset evaluation threshold value so as to predict a cognitive impairment patient. The system provided by the invention can more accurately and efficiently distinguish patients with mild cognitive impairment from healthy people, and can be used as an auxiliary diagnosis tool to be applied to primary screening of mild cognitive impairment on relatively large people (such as communities and physical examination centers).

The invention discloses a fetal heart uterine contraction automatic interpretation system based on network characterization, which comprises a network model, and the network model comprises a CV module, an AT module, an SW module and a TC module which are connected in sequence; the CV module encodes low-level spatio-temporal information in the CTG signal through three convolutional layers; the AT module comprises a plurality of self-attention layers; the TC module uses a time convolution network to extract advanced time features in a time sequence, and inputs the advanced time features into a full connection layer with a Sigmoid classifier. According to the method, a multi-representation and multi-model integration thought is adopted, related features beneficial to CTG interpretation are fully extracted, the problem that a manual feature extraction algorithm is insufficient in capturing a long-time-span dependency relationship is solved, subsequent various analysis and research on CTG signals are facilitated, and the method is suitable for popularization and application. More accurate and reliable diagnosis information is provided for doctors and applied to the field of medical signal processing, and a more accurate and reliable auxiliary diagnosis tool is provided for clinicians.

The invention provides an auxiliary diagnosis method and device based on multi-modal data and electronic device.The method comprises the steps that a multi-modal sample set and a sample diagnosis report are obtained, and the multi-modal sample set comprises sample eye movement features, sample electroencephalogram features, sample heart rate features and sample galvanic skin features; the sample diagnosis report comprises a sample diagnosis result, a sample clinical record and a sample treatment record; according to the multi-modal sample set and the sample diagnosis report, training to obtain a disease diagnosis model; acquiring multi-modal information of the patient; and inputting the multi-modal information of the patient into the disease diagnosis model to obtain a diagnosis result of the patient. According to the technical scheme provided by the embodiment of the invention, the disease diagnosis model is trained through the multi-modal information of a large number of patients and the corresponding diagnosis reports, and disease diagnosis is carried out through the disease diagnosis model, so that the multi-modal information of the patients is fully utilized, and the efficiency of disease diagnosis is also improved.

The invention discloses a marker for early colorectal neoplastic lesion diagnosis and application, and belongs to the technical field of biological medicine. Aiming at the problems of low colonoscopy coincidence rate of colorectal cancer patients at present and limited sensitivity and specificity of existing biomarker diagnosis, LncRNA sequencing research is combined with expanded sample verification to obtain high-specificity and high-sensitivity LncRNA which is highly related to colorectal neoplastic lesions (including adenoma and colorectal cancer), the LncRNA is specifically LOC401585, and the LncRNA has the advantages of high specificity, high sensitivity, high specificity and high sensitivity and can be used for detecting colorectal neoplastic lesions (including adenoma and colorectal cancer). The diagnosis efficacy AUC of the LOC401585 on the adenoma and the colorectal cancer is 0.952 and 0.876 respectively. Therefore, the blood LOC401585 marker can be used for auxiliary diagnosis of colorectal neoplastic lesions, and provides data support for finding novel small molecule drugs with potential treatment value.

Provided is an application of a circular RNA in the preparation of a cerebral stroke diagnosis product, belonging to the field of circular RNA medical treatments. Provided is a kit, comprising a reagent and / or test paper and / or gene chip for detecting a circular RNA-circ-Magi1. The nucleotide sequence of the circular RNA-circ-Magi1 is as shown in SEQ ID NO: 1 or SEQ ID NO: 4. Further provided is the application of a detection system for the diagnosis, assisted diagnosis or prognosis evaluation of cerebral stroke, and a detection reagent of the circular RNA-circ-Magi1 in the preparation of a kit, test paper, chip or high-throughput sequencing platform for the diagnosis, assisted diagnosis or prognosis evaluation of cerebral stroke.

The invention provides a method and a model based on transcriptome and intratumoral microorganisms, and particularly relates to a calculation model for evaluating cervical cancer lymph node metastasis risk and a related method, program, system and equipment. According to the model, abundance information of various gene markers (including CARD9, MNX1AS2, MRAS, OLFML2A and RPS28) and abundance information of multiple microorganisms (including Dialister, Catonella and Campylobacter) are fused as input characteristics, and a risk prediction model is constructed through a machine learning algorithm, so that quantitative evaluation on the lymph node metastasis risk of the cervical cancer patient is realized. The prediction precision of cervical cancer lymph node metastasis is improved, an auxiliary diagnosis decision basis is provided for clinic, and the method has important medical application value and industrial transformation potential.

The invention relates to the technical field of biological medicine, and provides application of TRIM69 in preparation of a kit for auxiliary diagnosis of systemic lupus erythematosus. The kit assists in diagnosing systemic lupus erythematosus by detecting the TRIM69 level. Through the mRNA expression quantity of the TRIM69 and the expression quantity and / or concentration of the protein, the auxiliary diagnosis of the systemic lupus erythematosus is realized, and the TRIM69 as a biomarker has important values in the aspects of auxiliary diagnosis, disease activity monitoring, prognosis evaluation, diagnosis efficiency improvement and the like. Not only can a doctor be helped to recognize a high-risk patient earlier, but also a basis can be provided for individualized treatment, so that the long-term prognosis of the systemic lupus erythematosus patient is improved.

The invention belongs to the technical field of biological medicine, and particularly relates to application of macrophage LSR gene and / or LSR protein in preparation of drugs and diagnostic products for preventing and / or treating diabetic nephropathy. Research finds that the LSR deletion of macrophages aggravates renal injury by promoting inflammatory response of diabetic nephropathy and increasing lipid renal toxicity; on the contrary, after the LSR expression of the macrophages is recovered, the inflammatory response and lipid droplet deposition of the kidney of the diabetic nephropathy mouse can be effectively reduced, and meanwhile, the kidney injury is improved. Cell experiments show that LSR deletion of macrophages can activate a YAP signal channel to increase secretion of inflammatory factors and reduce low-density lipoprotein uptake at the same time. Therefore, the macrophage LSR can be used as a diabetic nephropathy drug target, a gene therapy target gene and an auxiliary diagnosis marker, and a new theoretical basis and an intervention strategy are provided for prevention and treatment of the disease.

The invention relates to a multi-view fusion cervical cancer precancerous lesion image classification method based on self-supervised learning, and the method comprises the steps: collecting video screenshot data of a prostate endoscopic resection operation, and carrying out the preprocessing, and obtaining an optimized model weight; a second visual Transform model and a third visual Transform model which are optimized are obtained, and the second visual Transform model and the third visual Transform model which are optimized are obtained; and carrying out weighted fusion on the classification result of the colposcope acetic acid picture of the precancerous lesion and the classification result of the colposcope iodine picture of the precancerous lesion to obtain a final classification result. According to the method, a self-supervised learning method is introduced, and a large amount of label-free medical picture data is utilized, so that the generalization ability and classification accuracy of a ViT model, namely a visual Transform model, in cervical cancer precancerous lesion classification are improved; according to the method, efficient auxiliary diagnosis of CIN grading is achieved, especially in areas with limited medical resources, an efficient and low-cost solution is provided for automatic screening, doctors are helped to improve the diagnosis accuracy, the missed diagnosis rate is reduced, and important technical support is provided for early cervical cancer prevention.

The invention discloses a newborn rare disease clinical feature auxiliary diagnosis system based on deep learning, relates to the technical field of medical diagnosis, and aims to solve the problem of multi-dimensional high-dimensional data feature fusion, break through the generalization ability limitation of a traditional diagnosis model, accurately capture a hidden feature combination and improve the diagnosis accuracy. The neonatal rare disease diagnosis system comprises a medical database module used for storing medical training samples and medical data of a patient to be diagnosed; the deep learning system is used for carrying out feature extraction, coding, matching and diagnosis on the medical data; the diagnosis result visualization tool comprises a visualization tool and a textualization tool. According to the method, multi-dimensional high-dimensional data are fused through the feature embedding module, the generalization limitation of a traditional diagnosis model is broken through, and hidden feature combinations are accurately captured; the training feature coding module reduces the over-fitting risk, and for complex cases of methylmalonic acidmia, the diagnosis accuracy is greatly improved compared with a traditional method.

The invention discloses application of SMCP in preparation of a rheumatoid arthritis diagnosis product, an anti-SMCP antibody in a biological sample of a patient is used as a marker, and the diagnosis product is used for detecting the content of the anti-SMCP antibody. The SMCP is a polypeptide mixture which is prepared from citrullinated SR-A polypeptide, citrullinated MCV polypeptide, citrullinated caspase14 polypeptide and citrullinated PCOLCE polypeptide. According to the present invention, the anti-SMCP antibody can be subjected to one-time detection so as to achieve the accurate diagnosis of rheumatoid arthritis (RA); besides, the anti-SMCP antibody also has better auxiliary diagnostic value for RA patients with negative anti-CCP antibody, and when the anti-SMCP antibody and the anti-CCP antibody are combined for diagnosis, the RA detection rate can be further improved under the condition of keeping high specificity, and the targets of early diagnosis, early treatment and reduction of disability rate can be more favorably realized.

The invention discloses an oral microbial marker composition and application thereof in preparation of a kit for diagnosing high altitude headache, and particularly relates to the technical field of molecular biotechnology detection. A diagnosis model constructed on the basis of oral flora finds that the provided oral flora marker combination can specifically recognize patients with high altitude headache from plateau healthy people. The kit is used for predicting the high-altitude headache illness condition and clinical auxiliary diagnosis, is good in detection specificity and high in sensitivity, can indicate the condition of the high-altitude headache tissue microbial flora, and can guide the clinical diagnosis mode of the high-altitude headache and the adjustment of medication so as to reduce the probability of misdiagnosis and missed diagnosis in clinical diagnosis. The oral flora marker has important application in the aspects of screening and diagnosis of high altitude headache and the like, so that health damage and life risks caused by diseases are reduced.

The invention provides a sleep apnea and hypopnea intelligent monitoring and auxiliary diagnosis system and method based on a flexible sensor, and belongs to the technical field of sensors and respiration monitoring equipment. The system comprises a resistive flexible strain sensor, a wireless data acquisition card and a portable data processing terminal, a resistance-type flexible strain sensor is adhered to the skin surface of a main muscle group and an auxiliary muscle group related to human respiration, resistance signals related to the respiration frequency and amplitude are obtained, respiration data of a patient are collected in real time, and further, self-adaptive amplitude alarm upper and lower limits are constructed through statistical modeling of the individual resting respiration amplitude, so that the respiration alarm accuracy is improved. Therefore, the individual adaptability and robustness of the respiration detection system are improved.

This invention discloses a combination of biomarkers for the auxiliary diagnosis of chronic pain comorbid with depression and its application, belonging to the fields of in vitro diagnostics (IVD) and clinical testing technology. The biomarker combination includes interleukin-6 (IL-6), interleukin-12 (IL-12), and the concentration ratio of kynurenic acid to 3-hydroxykynurenine (KYNA / 3-HK). This invention also discloses the application of this biomarker combination in an in vitro diagnostic kit for chronic pain comorbid with depression. Experimental results show that the diagnostic efficacy of this biomarker combination (AUC=0.871) is significantly better than that of a single biomarker, effectively distinguishing patients with chronic pain comorbid with depression from those without chronic pain, providing an objective auxiliary diagnostic tool for clinical practice.

The invention provides an ischemic stroke auxiliary diagnosis system based on a peripheral blood T cell single cell transcriptome and application thereof, and the auxiliary diagnosis system comprises a peripheral blood mononuclear cell acquisition module, a single cell RNA sequencing module, a data processing module, a data analysis module and a result discrimination module. With a coding gene of a protein molecule significantly related to the occurrence of ischemic stroke as a target gene, a data processing module obtains the expression level of the target gene in each T cell; the data analysis module obtains a risk score of each T cell of the subject by using a built-in single cell risk scoring model, and weights the risk scores of all the T cells through distributed perception identification to obtain an individual risk score of the subject; and the result judgment module is used for classifying the subjects into ischemic stroke negative and ischemic stroke positive according to the individualized risk scores of the subjects. The auxiliary diagnosis system disclosed by the invention is high in ischemic stroke discrimination capability, and high in sensitivity and specificity.

The invention provides application of a CHD1L detection reagent in preparation of a drug-resistant breast cancer screening kit, and belongs to the field of biological medicine. Researches find that when the CHD1L gene expression level in cancer tissues of a breast cancer patient is high, the breast cancer patient is prone to drug resistance to paclitaxel drugs, and the treatment effect of the paclitaxel drugs is poor. The reagent for detecting the CHD1L gene expression level is used for preparing the drug-resistant breast cancer screening kit, can be used for auxiliary diagnosis of clinical breast cancer types, provides an effective basis for related treatment measures or decisions of patients, and has a good clinical application prospect.

This invention discloses a lncRNA biomarker associated with AML, characterized in that the lncRNA biomarker is lncRNA FOXN3-AS1, and the nucleotide sequence of lncRNA FOXN3-AS1 is shown in SEQ ID NO: 1. This invention also discloses the application of a detection kit and method in the diagnosis and prognosis of AML. The lncRNA FOXN3-AS1 of this invention, as an auxiliary diagnostic biomarker for clinical detection of AML, has the advantages of simple operation, low cost, and accurate detection, making it suitable for clinical screening. The biomarker provided by this invention can be applied to the preparation of early AML assessment products, which is beneficial for further elucidating the pathogenesis of AML and helps in the discovery of novel small molecule drug targets with potential therapeutic value.

The embodiment of the application discloses an echocardiogram auxiliary report generation method, system, device and storage medium, and relates to the technical field of medical treatment. The echocardiogram auxiliary report generation method first aligns the input echocardiogram video through automatic registration, then identifies the starting time of each cardiac cycle in the echocardiogram video, generates the echocardiogram video frame of each cardiac cycle based on a preset frame number, divides the heart structure in the echocardiogram video frame into N target images of target regions by using a medical image segmentation model, obtains a structure parameter, inputs the structure parameter into an echocardiogram knowledge graph to automatically generate an auxiliary report, including an auxiliary diagnosis report and / or an auxiliary interpretation report. Therefore, doctors do not need to manually measure and fill in related clinical parameters or diagnosis opinions, and the health status of patients can be understood, and the working pressure of doctors is effectively reduced.

This invention discloses a multimodal imaging and multidimensional index evaluation software and hardware system for scars, involving an infrared, RGB, and stereoscopic three-modal imaging hardware system, and an intelligent calculation and monitoring software system for more than twenty scar evaluation indicators. The intelligent evaluation system for scars includes indicators such as perimeter, length, width, thickness, area, volume, color, roughness, consistency, elasticity, blood flow density, risk of continued scar growth, and color difference. This forms an objective, multidimensional, precise, and intelligent evaluation system for the scar diagnosis and treatment process, thereby assisting in diagnosis and treatment, as well as monitoring and tracking the entire life cycle of scars. This contributes to the visualization, objectification, and accuracy of the scar diagnosis and treatment process, providing doctors and patients with comprehensive diagnostic and treatment evidence and enabling tracking and tracing.