372 results about "Aids diagnostics" patented technology

The present disclosure provides an intelligent decision reasoning method for type-based diagnosis and treatment of a cardiovascular disease, a device, and a product, and relates to the medical field. The present disclosure adopts a risk factor mining model of a cardiovascular disease and a drug attribute association mining model of the cardiovascular disease to extract relevant information from cardiovascular data, achieving precise prediction of a fine-grained cardiovascular disease type and effectively promoting precision medicine. Based on a clinical diagnostic dataset and a clinical symptom diagnosis knowledge system of the cardiovascular disease, a type-based auxiliary diagnosis model of the cardiovascular disease is constructed, and intelligent decision reasoning is performed to obtain an association rule. This achieves efficient integration of type-based decision support of the cardiovascular disease and a medical convalescence knowledge base of the cardiovascular disease, and enhances quality of a cardiovascular medical convalescence association rule set.

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 present invention discloses a cerebrospinal fluid sampling and detection device for neurology, and relates to the detection technology field of auxiliary diagnosis of neurological diseases. The present invention comprises a device body, a sampling device is arranged on one side of the device body, a measuring component is arranged on the top of the device body, and a gas injection device is arranged inside the device body. The present invention, through the arrangement of the sampling device, allows the interior of the liquid storage tube to be filled with the required cerebrospinal fluid, and no gaps are left, thereby ensuring that the cerebrospinal fluid in the liquid storage tube is the expected amount, and at the same time, the hollow plug rod, the liquid storage tube, the connecting tube, and the device body cooperate, so that the hollow plug rod fits the inner wall of the liquid storage tube, and the hollow plug rod will inject all the cerebrospinal fluid into the device body, thereby avoiding the residue of cerebrospinal fluid, resulting in the actual amount of cerebrospinal fluid injected into the device body being lower than expected, resulting in insufficient concentration of the final sample, affecting the problem of test results.

The invention relates to the technical field of in-vitro diagnostic reagent chemiluminescence detection, in particular to a Christmas liquefied carbohydrate chain antigen chemiluminescence detection kit and a preparation method thereof. The chemiluminescence detection kit comprises the following components: a KL-6 calibrator, an amino magnetic bead coupled KL-6 monoclonal antibody, an alkaline phosphatase labeled KL-6 monoclonal antibody, an AMPPD rapid chemiluminescence substrate solution and a cleaning solution, and the chemiluminescence detection kit prepared from the components is simple in detection process, easy to operate, high in automation degree and high in detection sensitivity. The kit is high in sensitivity, good in specificity and good in stability, a result can be automatically obtained within 12.5 min, the requirement for clinical auxiliary diagnosis of interstitial lung diseases can be met, and the problem that in the prior art, a chemiluminescence detection kit prepared by adopting an acridinium ester magnetic particle chemiluminescence immunoassay method or a biotin-streptavidin detection system is poor in specificity and high in sensitivity is solved. The technical problems of low marking efficiency, high manufacturing cost and poor detection result reliability in the prior art are solved.

The invention discloses a DNA methylation biomarker combination for prostate cancer detection and application of the DNA methylation biomarker combination, and belongs to the technical field of biological medicine. The DNA methylation biomarker combination comprises a combination of methylation sites indicated by CG in the SEQ ID NO: 1 sequence of the USP44 gene and the SEQ ID NO: 2 sequence of the HIF3A gene. It is reported for the first time that the methylation levels of the USP44 gene and the HIF3A gene are detected at the same time to conduct auxiliary diagnosis on the prostatic cancer, a sample urine detection result shows that the sensitivity is 97.14%, the specificity is 97.78%, and the non-invasive and reliable prostatic cancer detection method is provided and has good application prospects in early screening of the prostatic cancer.

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 invention discloses a teenager idiopathic scoliosis auxiliary diagnosis and evaluation system based on an X-ray film, and relates to the technical field of medical instruments. Comprising an image preprocessing module, a key point detection module, a Cobb angle measurement module and a Lenke typing module. According to the teenager idiopathic scoliosis auxiliary diagnosis and evaluation system based on the X-ray film, key point detection, automatic Cobb angle measurement and Lenke typing including bending type, lumbar vertebra correction type and thoracic vertebra sagittal view kyphosis typing of the standing position, the Bending position and the lateral position spine X-ray film are achieved, the accuracy, efficiency and reliability of auxiliary diagnosis are remarkably improved, and the system is suitable for popularization and application. And comprehensive support is provided for clinical diagnosis and treatment decision of scoliosis.

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 discloses a gastrointestinal tumor image multi-attribute classification method and device based on multi-modal fusion, and a medium. The method comprises the following steps: constructing a multi-modal gastrointestinal tumor data set; performing hierarchical operation on the data set; constructing an image feature extraction network, and respectively extracting EUS image and WLE image features in the data set by using the image feature extraction network; constructing a multi-attribute classification network, performing multi-attribute classification training on the tumor attributes by using the image features, and after the training is completed, predicting the tumor image to be detected by using the network to obtain the tumor attributes after network prediction completion; and performing cross-modal attention fusion and tumor property classification under lesion attribute guidance, and finally outputting tumor property classification of the to-be-detected tumor image. According to the method, through cross-modal attention fusion operation, the model learns complementary contents in the EUS mode and the WLE mode, the attributes of tumor deletion are supplemented, and the properties of the tumor of the patient are classified by utilizing the supplemented attribute data, so that the method is used for assisting a diagnosis result.

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 a cerebrospinal fluid sampling and detecting device for the neurology department, and relates to the technical field of detection of auxiliary diagnosis of neurology department diseases. The device comprises a device body, a sampling device is arranged on one side of the device body, a measuring assembly is arranged at the top of the device body, and a gas injection device is arranged in the device body. Through the arrangement of the sampling device, the liquid storage pipe is filled with required cerebrospinal fluid, no gap is reserved, it is ensured that the cerebrospinal fluid in the liquid storage pipe is the expected amount, meanwhile, the hollow plug rod, the liquid storage pipe, the connecting pipe and the device body are matched, and the hollow plug rod is attached to the inner wall of the liquid storage pipe; the hollow plug rod can completely inject the cerebrospinal fluid into the device main body, so that the problems that the amount of the cerebrospinal fluid actually injected into the device main body is lower than the expectation due to the residual cerebrospinal fluid, the concentration of a final sample is insufficient, and the detection result is influenced are solved.

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 a training method of an auxiliary diagnosis model and a disease symptom collection method and device, and relates to the technical field of medical data processing.The method comprises the steps that a plurality of sample data sets of preset diseases are obtained, randomly generating a simulated initial symptom description statement corresponding to each piece of sample data according to the characteristics of multiple symptoms of one patient in each piece of sample data in the sample data set of the preset disease by adopting a patient simulation large model, and carrying out initial diagnosis on each piece of sample data by adopting an initial auxiliary diagnosis model according to the simulated initial symptom description statement and each piece of sample data, and generating a simulated disease symptom corresponding to each piece of sample data, and training the initial auxiliary diagnosis model according to the simulated disease symptom and the preliminary diagnosis information to obtain a target auxiliary diagnosis model. According to the invention, the application prospect of the auxiliary diagnosis model is expanded, so that the disease symptoms of the target patient can be collected more comprehensively and fully.

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 relates to an intelligent cervical cancer screening method for multi-mode traditional Chinese medicine and western medicine combined data, which comprises the following steps: S1, data integration: respectively collecting text data in traditional Chinese medicine and western medicine diagnosis and treatment processes, and combining the two kinds of data to form a comprehensive patient health text file; s2, carrying out fine tuning training on the obtained traditional Chinese and western medicine combined data, and carrying out preliminary screening and diagnosis on cervical cancer; s3, based on an image segmentation and classification model, performing effective segmentation on the image of the cervical cancer, giving a model recommendation category, and promoting a doctor to re-judge the cancer condition; and S4, based on output results of the text and the image data, giving a related treatment scheme. According to the method, automatic classification and focus segmentation auxiliary diagnosis of cervical cancer images are realized, the medical history of a patient, instructions given by a doctor and the like are integrated, a comprehensive diagnosis platform is constructed, preliminary diagnosis suggestions are provided, a personalized treatment scheme is generated according to the specific condition of the patient, and a treatment plan is dynamically adjusted.

The invention belongs to the field of biological detection, and particularly relates to application of an ON detection reagent in auxiliary diagnosis of acute chest pain diseases. It is found that the ON protein is closely related to acute chest pain such as AAD, AMI and APE, the ON serves as a marker, a detection reagent of the ON is prepared into an auxiliary diagnosis product, and great clinical value and practical significance are achieved for early diagnosis and long-term prognosis of the three diseases (especially AAD) and improvement of life quality.

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.