756 results about "Diseases/diagnoses" patented technology

The invention relates to the technical field of artificial intelligence and medical diagnosis, in particular to a disease diagnosis prediction method and system based on a graph neural network. The disease diagnosis prediction method based on the graph neural network comprises the five steps of heterogeneous medical knowledge graph construction, adaptive node embedding representation, hierarchical graph attention network modeling, incremental learning dynamic graph updating and multi-dimensional feature input and result output. The invention discloses a disease diagnosis and prediction system based on a graph neural network. The system comprises a multi-source data acquisition module, a heterogeneous graph construction module, a self-adaptive embedding module, a graph network calculation engine, a dynamic updating module, a disease prediction module and a feedback optimization module. According to the method, the multi-modal heterogeneous knowledge graph is constructed to integrate the multi-dimensional data of the patient, and the hierarchical graph attention network and the dynamic incremental learning are combined, so that the accurate prediction of the disease risk and the visual explanation of the pathological association path are realized.

The invention relates to the technical field of skin disease diagnosis, and discloses an AI auxiliary decision support system for skin disease diagnosis, which integrates an image acquisition module, an image analysis module and a comprehensive analysis and diagnosis decision module. A high-definition image of the skin of a patient is obtained through high-precision skin photographing equipment, and a training data set is formed by combining basic information of the patient. A deep learning algorithm is utilized to train a skin disease recognition model and an illness state evaluation model, and accurate recognition of skin disease types and objective evaluation of severity are realized. And the comprehensive analysis module fuses output of the two models, constructs a fusion decision model and provides comprehensive diagnosis suggestions. And the diagnosis decision module is used for generating a disease type judgment and illness state evaluation report based on the trained model in combination with real-time image data so as to effectively assist doctors in making decisions. The accuracy, the efficiency and the popularization degree of skin disease diagnosis are improved, and complex and changeable diagnosis requirements in clinical practice are met.

A medical image disease diagnostic system that is included in a clinical medical diagnosis workflow. In one embodiment, a user interface is configured to assess a medical imaging application that provides for viewing, analyzing, and annotating of medical images and medical non-image data and medical training data. A processor includes a pre-trained subsystem configured to generate AI pre-trained models based on the medical image and medical non-image data, a training data generation subsystem configured to generate the medical training data, and a medical image model training subsystem configured to transform the medical image and medical non-image data into the medical training data. The medical image model training subsystem is further configured to train or generate the pre-trained AI models based on the medical training data, and a model drift subsystem is configured to determine accuracy of the pre-trained AI models. A diagnostic reporting subsystem configured to generate clinical diagnostic reports.

The invention discloses a medical and educational informatization comprehensive supervision and diagnosis platform system based on multi-modal data fusion. A data acquisition module acquires multi-modal data from a medical and educational information system and preprocesses the multi-modal data; the data storage module constructs a hybrid architecture to classify and store data and establishes an index. The multi-modal data fusion module deeply fuses medical records, images and physiological monitoring data of medical treatment and teaching resources, learning behaviors and teaching evaluation data features of education by means of a feature extraction technology and a fusion algorithm. The supervision and diagnosis module constructs models of medical service quality evaluation, disease diagnosis assistance, teaching quality monitoring and the like based on fusion features to realize accurate analysis. The user interaction module provides interaction interfaces for different roles and supports operations such as data query and uploading. The platform system breaks the data barrier, fully mines the data value, provides powerful support for informatization management, decision making and service optimization in the medical and education fields, and promotes the innovative development of the two fields.

The invention discloses a medical image segmentation guide classification system for disease diagnosis, and the system comprises a data importing module which is used for reading and preprocessing disease image data and disease category data, and obtaining an original image, a real semantic segmentation mask image and a real disease category label Label; and the lesion semantic segmentation and disease classification module is used for executing a segmentation task and a classification task on the original image obtained by the data import module by adopting an improved VM-UNet network to obtain a predicted semantic segmentation mask image and a predicted disease category, so that accurate lesion semantic segmentation and accurate disease classification of the medical image are realized. According to the method, the semantic segmentation task and the classification task of disease diagnosis can be optimized at the same time, implicit segmentation guide classification is realized by using the advantage of multi-task learning, and the shape information and texture information of a focus of interest can be enhanced by reusing a prediction semantic segmentation mask image, so that explicit segmentation guide classification is realized, and the classification efficiency is improved. And the method has relatively high calculation efficiency and generalization.

The invention discloses a medical text-oriented intelligent disease diagnosis causal atlas construction method and system. The method comprises the steps of medical text related data acquisition, causal entity identification, causal relationship extraction and entity unification. The model and the method for automatically constructing the causal knowledge graph are realized, the functions of dynamic uncertain causal knowledge graph editing, clinical intelligent auxiliary diagnosis, automatic extraction of electronic cases and the like are provided, and multi-source fusion of medical text data and expert experience knowledge is realized, so that the accuracy and the reliability of the system are improved. And a disease causal knowledge graph with a certain clinical application value can be established.

The invention relates to the technical field of ophthalmology medical diagnosis, and discloses a three-dimensional reconstruction-based fundus focus intelligent labeling method and diagnosis system. The method comprises the following steps: receiving multi-modal image data streams such as fundus color photos, OCT images and FFA images of an ophthalmological patient; performing spatial registration and feature fusion by using a pre-trained lesion feature fusion model to generate a three-dimensional lesion probability distribution diagram and a lesion category confidence matrix; constructing an adaptive annotation threshold model to generate a multi-modal annotation instruction set; based on the focus development chain model, focus development is simulated, and instruction set parameters are optimized and labeled; and iteratively optimizing through a distributed reinforcement learning framework, and outputting the focus labeling action sequence to an ophthalmology diagnosis platform. According to the method, multi-modal image information can be integrated, the diagnosis accuracy and efficiency are improved, personalized diagnosis is realized, resources are reasonably utilized, and powerful support is provided for ophthalmic disease diagnosis.

Systems and methods are disclosed for using vessel reactivity to guide diagnosis or treatment for cardiovascular disease. One method includes receiving a patient-specific vascular model of a patient's anatomy, including at least one vessel of the patient; determining, by measurement or estimation, a first vessel size at one or more locations of a vessel of the patient-specific vascular model at a first physiological state; determining a second vessel size at the one or more locations of the vessel of the patient-specific vascular model at a second physiological state using a simulation or learned information; comparing the first vessel size to the corresponding second vessel size; and estimating a characteristic of the vessel of the patient-specific vascular model based on the comparison.

The invention discloses a multi-modal data fusion method and device, equipment and a storage medium, and relates to the technical field of multi-modal data fusion, and the method comprises the steps: obtaining a target full-slice image and target clinical data, and obtaining corresponding clinical feature information based on the target clinical data; obtaining each piece of initial feature information corresponding to the target full-slice image by using the target multi-modal fusion diagnosis model and each preset transformation function, and carrying out weighted fusion operation on each piece of initial feature information to obtain image feature information; and dynamically adjusting weights of the clinical feature information and the picture feature information based on a preset attention gating mechanism, and performing adaptive fusion on the clinical feature information and the picture feature information to obtain a target multi-modal feature. The attention gating mechanism is utilized to control the data of different modes to perform adaptive fusion, so that the data of different modes can perform adaptive weight adjustment to perform fusion, the reliability of multi-mode features is improved, and the accuracy of a disease diagnosis result is further improved.

The invention discloses a neurodegenerative disease diagnosis method for constructing a dynamic brain network based on adaptive graph structure learning, and belongs to the technical field of artificial intelligence. The invention provides a self-adaptive graph structure learning multi-level nerve disease diagnosis framework, which can automatically learn and discover complex and deep connection modes among a plurality of brain areas so as to construct a dynamic brain network. According to the method, a global structure of a brain network is embedded into brain network construction based on a time window through a window mapping module, so that richness and accuracy of brain network topology feature representation are improved; in the fusion process of window features, a learnable time graph convolution module is provided to automatically capture time connectivity across time windows and effectively integrate high-order dynamic topological features extracted from different time windows. And finally, disease diagnosis and classification are realized by using a multi-layer perceptron. According to the method, the identification and classification accuracy of the neurodegenerative diseases under the fMRI data can be effectively improved.

The invention provides a common mental disease intelligent diagnosis method and system based on multiple agents, and relates to the field of medical artificial intelligence. The method comprises the following steps: S1, extracting diagnosis standards and symptom characteristics of common mental disorders, and constructing a similar patient knowledge base after verification and evaluation of the similar patient knowledge base and expert calibration; s2, acquiring clinical data from a hospital information system, and performing large medical record structuring, clinical scale simplification and scale score analysis on the clinical data to form a similar patient database; and S3, performing symptom matching and scale performance analysis on the input clinical data, constructing a multi-agent mental disease diagnosis framework, and performing multi-agent diagnosis debate based on the multi-agent mental disease diagnosis framework. The technical problems of symptom overlapping and diagnosis subjectivity among common mental diseases are solved by constructing a multi-agent cooperative diagnosis framework, introducing particle size symptom analysis and dynamically integrating structured authoritative medical diagnosis standards.

The invention provides a medical diagnosis system based on chain reasoning and multi-modal fusion and application thereof, and belongs to the technical field of medical care informatics. The system comprises a data preprocessing module, a data fusion module, a model reasoning module and an output module, and can perform deep processing and intelligent analysis on multi-modal medical data (including text data, medical image data and structured data) of a patient. And the output module provides a diagnosis result and a detailed reasoning process, so that the interpretability and the credibility of the diagnosis result are improved. Through multi-modal data fusion, a chained reasoning mechanism and optimized loss function design, the accuracy, generalization ability and interpretability of disease diagnosis are remarkably improved, the limitation of the traditional technology in the aspects of single-modal analysis and black box reasoning is broken through, transparent, efficient and intelligent diagnosis decision support is provided for clinicians, and the clinical application prospect is wide. And intelligent and precise development of medical diagnosis is promoted.

The invention relates to the technical field of medical image processing, and discloses a method for intelligently describing liver space-occupying lesion ultrasonic image content by using LLM. A liver ultrasonic image sequence, a patient historical medical record text and a blood biochemical index vector are obtained through a multi-modal data acquisition module, and features are extracted through a cross-modal contrast learning network to generate embedded vectors and align the embedded vectors. And inputting the aligned image embedding vector into a dynamic context sensing decoder, and generating a description text semantic mark sequence by using a layered multi-head attention mechanism. The confidence coefficient is evaluated through an uncertainty calibration module, and the text is optimized through a post-processing reordering mechanism when the confidence coefficient is lower than a threshold value. A real-time interaction optimization mechanism is further arranged, and the model is updated according to feedback of doctors. According to the method, multi-modal data are fused, description accuracy and reliability are improved, text quality is optimized, clinical requirements are met, and liver disease diagnosis is assisted.

The invention relates to the field of intelligent assistance, and particularly discloses an intelligent assistance system for child pneumology department disease diagnosis, which obtains doctor inquiry record information of a to-be-diagnosed child, and in response to existence of auxiliary examination items, obtains auxiliary examination information of the auxiliary examination items of the to-be-diagnosed child, and sends the auxiliary examination information to the to-be-diagnosed child. Carrying out segmentation processing and global semantic coding aggregation on doctor inquiry record information by adopting an artificial intelligence-based image recognition and data analysis technology, and carrying out implicit semantic feature extraction on auxiliary examination information; and intelligently obtaining an auxiliary diagnosis result of the respiratory disease type of the child to be diagnosed according to a cross-modal semantic joint feature between a global semantic aggregation feature of the doctor inquiry record information and an auxiliary examination implicit semantic feature, and displaying the result. Therefore, the accuracy and comprehensiveness of auxiliary diagnosis of children respiratory diseases can be remarkably improved.

The invention discloses a multi-agent large-model disease diagnosis knowledge reasoning system based on data dual drive, and relates to the technical field of artificial intelligence assisted medical diagnosis. The system collects patient symptom follow-up records, laboratory test results, observation diagnosis probabilities and expert diagnosis recommendation results in a multi-source manner; time sequence evolution characteristics are extracted, a time sequence diagnosis sensitivity coefficient is calculated, and early recognition of disease risks is achieved; in combination with anti-fact simulation and statistical reasoning, a causal consistency coefficient is obtained and is used for verifying causal reasonability of observation diagnosis and contrast results; based on agent group consensus analysis, calculating a game consistency coefficient for judging the credibility of a diagnosis conclusion; positioning and multi-level verification are carried out on abnormal reasoning steps and knowledge fragments, so that the reliability and safety of a result are guaranteed; continuous optimization of the diagnosis model is realized through a log analysis and knowledge backflow mechanism; according to the invention, the accuracy, interpretability and safety of disease diagnosis can be obviously improved.

The invention discloses a multi-modal diagnosis and treatment scheme recommendation system based on reinforcement learning, and relates to the technical field of artificial intelligence, and the system comprises a data processing module which is used for obtaining multi-modal medical data of a patient and carrying out the preprocessing of the multi-modal medical data to obtain a multi-modal data set; the feature extraction and fusion module is used for performing feature extraction and fusion based on the multi-modal data set to obtain multi-modal features as the current state of the patient; the training set acquisition module is used for constructing a decision network and inputting the current state of the patient to obtain a current diagnosis and treatment scheme, taking the current state of the patient and the current diagnosis and treatment scheme as data groups, and circulating the process to obtain a plurality of data groups to form a training set; the network optimization module is used for optimizing the decision network based on the training set and the core objective function to obtain an optimized decision network; and the optimal scheme output module is used for obtaining the current state of the patient to be recommended and inputting the current state into the optimized decision network to obtain an optimal diagnosis and treatment scheme. And the disease diagnosis accuracy and comprehensiveness are improved.

The invention provides an integrated electrochemical biosensor capable of detecting double miRNA with high sensitivity, and the integrated electrochemical biosensor can simultaneously detect double miRNA markers with high sensitivity through self-assembly of a Y-shaped capture probe, preparation of an Au / TiO2-FTO array electrode, catalytic hairpin amplification (CHA) and construction of a micro-droplet chip electrochemical biosensor. The advantages of equalization of double-target miRNA capture probe sites, excellent electrode performance, reliable signal amplification, low sample consumption and the like are integrated, the linear range of miRNA21 and miRNA155 detection is 10 fM to 100 nM, and the detection limits are 5.00 fM and 5.17 fM respectively. By detecting the miRNA21 and miRNA155 double markers, the accuracy of breast cancer diagnosis is up to 98%, and the recognition sequences at the two ends of the Y stent probe are modified, so that the sensor can be used for diagnosing other miRNA marker related diseases. The integrated micro-droplet chip electrochemical biosensor constructed by the invention has a remarkable clinical diagnosis value and a prominent practical application prospect.

The invention discloses a medical auxiliary diagnosis method and system based on a convolutional neural network, and relates to the field of medical big data analysis, and the method comprises the steps: collecting various types of medical data, which comprise medical image data, electronic medical record text data and vital sign monitoring data; constructing a convolutional neural network model with a multi-branch structure; preparing a training data set containing labeled samples, wherein labeled information comprises disease diagnosis results and disease severity grades; training the convolutional neural network model by using the training data set, dynamically adjusting the learning rate in the training process by adopting a stochastic gradient descent optimization algorithm and combining a learning rate attenuation strategy, and introducing a regularization method; inputting to-be-diagnosed medical data into the trained convolutional neural network model, and outputting disease diagnosis probability distribution and disease severity prediction results through forward propagation calculation of the model; and generating a medical auxiliary diagnosis report according to the output prediction result.

The invention relates to the technical field of metabolite detection, in particular to a high-flux metabolite detection method and application thereof. According to the detection method, metabolites in a biological sample to be detected are detected by using a liquid chromatography-mass spectrometry technology, wherein mass spectrometry is quadrupole electrostatic field orbitrap high-resolution and triple quadrupole mass spectrometry; the mass spectrum conditions of the MRM ion pair are as follows: acquiring data in an NFSWI-DDA, NFSWI-DIA or DIA mode in a positive and negative ion mode, and acquiring and annotating the MRM ion pair; the MRM list is transited to triple quadrupole mass spectrometry, and the mass spectrometry conditions of the triple quadrupole mass spectrometry include verification and detection of MRM ion pairs in positive and negative ion modes. The detection method can meet the requirements of precise medical research on metabolite coverage rate, quantitative stability and detection flux, can be used for semi-quantitative and quantitative combined detection of metabolites in various biological samples, has applicability, and can provide technical support for disease diagnosis, drug development and biomarker discovery.

The invention discloses an AI-driven disease diagnosis and curative effect monitoring analysis system, which comprises a data processing module used for collecting multi-source diagnosis and treatment data and constructing a multi-modal sample set; the diagnosis prediction module is used for generating a diagnosis label, a diagnosis confidence value and a prediction curative effect trend through a multi-task learning model; the curative effect comparison module is used for collecting actual curative effect data, constructing a curative effect observation sequence and generating a curative effect deviation sequence based on the curative effect observation sequence and the predicted curative effect trend; the credibility evaluation module is used for executing credibility review based on the curative effect deviation sequence and the diagnosis confidence value; and the path evolution module is used for recording continuous multi-round diagnosis correction results, generating a jump type diagnosis path and updating a diagnosis and treatment data chain. The dynamic closed-loop verification mechanism between the multi-mode diagnosis and treatment data and the predicted curative effect trend improves the accuracy of disease diagnosis, the reliability of curative effect prediction and the adaptability of the diagnosis and treatment process.

The invention belongs to the field of liver cancer liver transplantation, and provides a liver cancer liver transplantation post-operation prognosis prediction system, which comprises a data acquisition module used for acquiring clinical data of a liver cancer patient from a plurality of data sources, including patient basic information, disease diagnosis information, drug use information and patient body function information; the data processing module is used for preprocessing the collected data, including data cleaning, missing value filling, data standardization and data integration, so as to ensure the quality and consistency of the data; by integrating multi-source data and combining advanced algorithms and models, accurate prediction of prognosis of patients after liver cancer liver transplantation is achieved, warning is automatically given out when a risk threshold value is reached, timely and accurate early warning information is provided for medical staff, and by collecting medicine use information and patient body function information, the patient prognosis is accurately predicted. And the physical condition of the patient can be obtained in real time through long-term monitoring, so that the prognosis prediction result is more accurate.

The invention relates to an automatic image segmentation and recognition system for the radiology department, and relates to the technical field of intelligent medical treatment. Comprising the following steps: an image preprocessing module provides a high-quality image for subsequent analysis by applying a multi-scale transformation and adaptive filtering technology; the feature extraction and fusion unit accurately excavates features by means of a CNN and an attention mechanism; the disease diagnosis reasoning engine is combined with the RNN and a medical knowledge base to deduce disease details; and the personalized treatment scheme recommendation module customizes a scheme by using reinforcement learning according to diagnosis and patient information. In addition, the system also comprises a virtual case generation unit, a model interpretability analysis unit, a self-supervised pre-training unit, a continuous learning unit, a distributed cooperative training architecture and a cross-platform adaptive interface. The method comprises the steps of collecting and standardizing image data, inputting the image data into a system to obtain diagnosis and schemes, and visually displaying auxiliary decision-making. The system efficiently processes radiology department images, improves diagnosis accuracy, optimizes a treatment scheme, adapts to multi-center and multi-platform requirements, and is expected to promote intelligent development of radiology department diagnosis and treatment.

The invention discloses an Alzheimer's disease deep joint learning diagnosis method based on multi-modal feature fusion, and provides a new method for constructing a 3D fusion image through multi-modal feature fusion of an MRI-p value under the condition that a 3D image structure is not damaged. End-to-end deep joint learning diagnosis based on a residual neural network and an attention mechanism is planned to be constructed, and feature information and position information related to AD are automatically learned to obtain a classification basis of diagnosis. Experiments include classification of three levels, respectively, disease diagnosis based on AD and HC, AD and MCI, and MCI and HC. By introducing gene data, feature information of disease diagnosis can be expanded, and more comprehensive feature representation is formed, so that a diagnosis model is promoted to better capture complex features of diseases. And inputting the single-mode MRI into the deep joint learning diagnosis model to carry out an experiment so as to verify the effectiveness of the multi-mode fusion image information.

The present invention provides a method for evaluating urinary system diseases based on urine detection, which belongs to the technical field of urology. The method for evaluating urinary system diseases based on urine detection comprises: adding corresponding reagents to three urine samples in sequence according to a preset time point sequence, and obtaining urine sample images after each addition of the reagents; performing image preprocessing on each acquired urine sample image, extracting image features including color changes, sediment morphology and distribution; establishing a time series feature data set, arranging and combining the image features corresponding to each time point in time sequence; using a pre-trained image recognition model to analyze the image features in the time series feature data set, and obtaining feature vectors; the method can solve the problems of high examination cost, great trauma, long examination time, limited diagnostic accuracy, etc. that the existing means of diagnosing urinary system diseases generally have.

The invention provides an AI-based medical record diagnosis and surgical operation code intelligent generation method and system, and relates to the technical field of medical information, and the method comprises the steps: obtaining multi-modal data from a hospital information system, including a text medical record, a medical image, a surgical video and a pathological report; entities and relationships are extracted through a multi-modal fusion model, and a hospital-level disease knowledge graph containing space-time dimensions is constructed; training a coding rule generation model and a verification model based on a reinforcement learning framework, and generating candidate codes and confidence scores thereof; and correcting the candidate codes according to manual feedback, and dynamically updating the knowledge graph and the rule base by using the graph neural network. Accurate disease diagnosis codes and surgical operation codes are generated in an intelligent mode, and the accuracy of the codes can be remarkably improved through automatic medical record code recommendation.

The invention relates to a fish disease detection method based on a deep learning image processing technology. The method comprises the following steps: collecting a fish disease image and carrying out image preprocessing; segmenting the preprocessed image by adopting a layered Mama model so as to extract a fish disease feature region; and performing feature extraction on the segmented disease feature region by using a MobileNet model, and performing disease classification through a full connection layer to obtain probability distribution of disease categories. According to the method, the hierarchical Mama model is adopted for image segmentation, the MobileNet model is adopted for disease classification, the fish disease detection efficiency and accuracy can be effectively improved, meanwhile, the challenges of poor image quality, limited training data and fish disease diversity can be coped with, and high-precision segmentation results and recognition are achieved under the condition of limited training data. According to the invention, accurate disease diagnosis can be provided, breeding operators can be helped to take measures in time, and loss caused by diseases is reduced.

The invention discloses a multi-modal dual-path fusion network structure based on data level fusion, and relates to the technical field of dual-path fusion network structures, comprising the steps of data set construction and preprocessing, selection and processing of a plurality of data sets, and image enhancement and normalization; constructing a network structure, constructing a dual-path network, pre-training on different data sets by using a mainstream backbone network, optimizing training, splicing feature layers, and performing classified training; and performing data fusion and classification, performing horizontal fusion on the double-path features, determining the weight by adopting a weighting strategy, and inputting the fused features into a full connection layer for classification according to a softmax function. According to the method, the ophthalmic disease diagnosis accuracy and model robustness are improved, the generalization ability is improved, network training is optimized, and medical intelligent diagnosis development is promoted.

The invention discloses a heterogeneous federated learning method based on contribution weighted aggregation, and the method comprises the steps: evaluating the distribution difference and quantity of data provided by heterogeneous equipment to obtain a contribution degree distribution weight, employing the weighted aggregation based on the contribution degree, a personalized method based on regularization, and a differential privacy protection technology, the method is realized through a data contribution evaluation module, a regular optimization module, a differential privacy module and a weighted aggregation module. According to the method, data sharing and model training among different medical institutions can be promoted while the privacy of the patient is protected, the accuracy of disease diagnosis and the formulation of a personalized treatment scheme are improved, and cross-institution collaborative learning is realized.

The invention relates to the technical field of disease diagnosis, and comprises a disease diagnosis method and system based on neural network cognitive diagnosis, and the method comprises the following steps: obtaining medical image data, calculating the gray level change rate, gradient direction distribution and edge continuity of a lesion region, counting the lesion tissue damage area, and analyzing the pathological tissue damage degree. And obtaining a lesion distribution consistency index. According to the invention, through comprehensive calculation of the medical image data and the pathological tissue slice data, fine-grained description of a focus area is realized, association among different lesion features is realized, and calculation of a lesion propagation path matching rate is realized, so that identification of lesion diffusion conditions is more accurate, and development trends of diseases in different tissue structures can be effectively predicted; the calculation of the cross-modal feature error is combined with the adjustment of the distribution weight of the lesion region, misdiagnosis caused by modal difference and extraction of abnormal signals in a lesion diffusion range are reduced, so that screening of potential diseases is more targeted, and the accuracy of disease recognition is improved.

The invention relates to the technical field of disease diagnosis, in particular to a sacroiliac joint lesion intelligent identification method and system based on MRI, and the method comprises the steps: data preparation and enhancement, multi-expert collaborative labeling, mixed 3D-2D model processing, verification and optimization, and deployment and interaction. Compared with the prior art that a scheme for detecting the sacroiliac joint lesion by adopting a single 3D CNN or 2D CNN model has the problems of inaccurate positioning of an anatomical structure and loss of inter-layer feature association, resulting in insufficient sensitivity and specificity, the method provided by the invention has the advantages that through an improved hybrid 3D-2D model architecture, spatial features are extracted by utilizing the 3D CNN and an inter-layer dependency relationship is modeled in combination with Transform, so that the sensitivity and the specificity of the sacroiliac joint lesion detection are improved, and the detection accuracy of the sacroiliac joint lesion detection is improved. Meanwhile, spatial pyramid pooling and a channel attention module are adopted to enhance the multi-scale feature extraction capability, so that the method has the advantages that the lesion detection sensitivity and specificity are remarkably improved, the bone marrow edema region can be more accurately recognized, and the misdiagnosis rate is reduced.