977 results about "Aided diagnosis" patented technology

The invention discloses a multi-modal diagnosis and treatment data analysis and auxiliary diagnosis method for internet medical treatment, which comprises the following steps: S1, performing feature extraction and fusion on multi-modal diagnosis and treatment data through a nonlinear mapping algorithm based on quantum state optimization; s2, constructing a self-adaptive dynamic relation network, and capturing a complex interaction relation between cross-domain features in the diagnosis and treatment data; s3, fusing implicit features in the sparse medical data with semantic association; s4, optimizing the diagnosis and treatment decision process by applying a causal inference technology; s5, processing anomalies in the diagnosis and treatment data by using a fusion generative adversarial network and sparse tensor decomposition technology; s6, constructing a decentralized encrypted federated learning network to protect the privacy of the patient; s7, predicting a disease evolution path in real time to form dynamic decision support; and S8, performing accelerated analysis on the high-dimensional feature mode in the diagnosis and treatment data in combination with quantum calculation. The method has the advantages of high data fusion capability, high diagnosis precision and high real-time decision support capability.

The invention discloses a children MPP (Mycoplasma Pneumoniae) auxiliary diagnosis system based on multi-modal time series data modeling, and aims to improve the auxiliary diagnosis, clinical typing and severe prediction capabilities of mycoplasma pneumonia and reduce the workload of doctors. The MPP dynamic representation method based on multi-modal time sequence modeling is innovatively provided, multi-modal data such as images, electronic medical records and inspection indexes are fused, the time sequence feature embedding technology is adopted, and the evolution trend of the disease course can be effectively captured. Furthermore, deep fusion of different modal data is realized by using a progressive cross-modal semantic alignment strategy and a modal attention gating mechanism, and the problem of insufficient diagnosis accuracy caused by incompatibility of data modals in a traditional method is solved.

The invention discloses an inquiry model training method and device based on a long thinking chain, and relates to the field of large models, semantic information is extracted through strategy network analysis of a model, and an initial step decision is generated in combination with context information in a historical memory library; sending the initial step decision into a reasoning path generator, and reasoning to generate a primary diagnosis disease source and an intermediate diagnosis step; sending the primary diagnosis source and the intermediate diagnosis step into a verification module, performing pathological logic verification according to a case diagnosis report and a medical knowledge base, and feeding back a verification result; the reasoning path generator updates the historical memory bank based on the feedback result, the preliminary diagnosis disease source and the intermediate diagnosis steps; the strategy network continues reasoning based on user feedback input and the updated context information in the historical memory bank, and finally an inquiry result is output. According to the scheme, technical means such as reinforcement learning, self-adaptive backtracking and memory enhancement are introduced into a long thinking chain reasoning framework, so that a large language model realizes multi-aspect comprehensive improvement in medical question and answer and auxiliary diagnosis scenes.

The invention belongs to the technical field of aviation energy design, and particularly provides an aviation energy power device fault intelligent inquiry system and method based on a knowledge graph. The method comprises the following steps: firstly, showing a complex data structure and a knowledge relationship to a user in a visual and dynamic manner through knowledge extraction, knowledge graph database construction, knowledge graph visualization and other methods, and constructing a knowledge graph basic framework; according to the method, the depth information of the target object can be extracted from the images from different angles, the fault characteristics of the equipment can be intuitively displayed, and the fault state of the equipment can be visually represented by extracting the depth information of the target object from the images from different angles. The evolution of the equipment state can be tracked through the change of the three-dimensional space; a conversion algorithm of the knowledge graph and the Bayesian network is realized through the Bayesian network, and rapid reasoning of fault causes is realized in combination with a Bayesian network reasoning algorithm; and finally, based on an intelligent inquiry system of a large model, a deep learning technology and a large-scale training model are utilized, and previous fault data and professional knowledge of the aviation energy power device are combined to provide intelligent auxiliary diagnosis support.

The invention discloses a medical image processing method and system based on artificial intelligence, and the method comprises the steps: carrying out the voice input of focus information through a voice input terminal, carrying out the preprocessing and character conversion output based on the inputted voice information, and dynamically generating a keyword set highly related to a current diagnosis task by combining structured priori knowledge of a medical knowledge graph and medical image-text multi-modal features, then generating a candidate diagnosis set based on the keyword set, calculating each semantic alignment score with the medical image features, generating a final diagnosis opinion through Langevin Dynamics sampling, and obtaining a final diagnosis result. And finally, the user terminal performs re-checking and enhanced correction, dynamically optimizes model parameters, and outputs a final formal diagnosis report based on an optimization correction result, thereby solving the accuracy and quality problems of traditional artificial intelligence on medical images, and realizing high-precision, high-robustness and interpretable artificial intelligence auxiliary diagnosis on the medical images.

The invention belongs to the technical field of medical artificial intelligence, and discloses an artificial intelligence-based auxiliary diagnosis and treatment system, which comprises a multi-modal data acquisition module, a dynamic learning module, a diagnosis reasoning module, a privacy protection module, an interactive decision module and an early warning monitoring module, the output end of the multi-modal data acquisition module is connected with the input end of the privacy protection module, the output end of the privacy protection module is connected with the input end of the dynamic learning module, the output end of the dynamic learning module is connected with the input end of the diagnostic reasoning module, and the output end of the diagnostic reasoning module is connected with the input end of the interactive decision module. And the early warning monitoring module monitors abnormal data in real time and performs bidirectional interaction with the diagnosis reasoning module. According to the method, multi-source medical data are integrated, and high-precision real-time auxiliary diagnosis is realized by adopting a dynamic incremental learning and privacy encryption technology; the medical worker cooperation efficiency is improved through an interactive interface, the safety is guaranteed in combination with real-time monitoring and early warning, and the system can remarkably improve the diagnosis and treatment efficiency and accuracy.

The invention relates to the technical field of battery fault detection, and particularly discloses a new energy automobile battery fault intelligent diagnosis method and system, and the method comprises the steps: collecting the three-dimensional time sequence data flow of the voltage, temperature and internal resistance of a battery pack through a distributed sensor array; the environment temperature, the number of charge and discharge cycles and the vehicle operation condition parameters are synchronously integrated as auxiliary diagnosis dimensions, and a multi-source heterogeneous data sensing system is constructed; the method comprises the following steps: preprocessing original data by adopting a dual-channel hybrid filtering architecture based on a multi-source heterogeneous data sensing system, and establishing an adaptive filtering parameter adjustment mechanism to generate multi-modal data; according to the method, multi-dimensional time sequence data and auxiliary diagnosis parameters of the battery pack are comprehensively collected through a constructed multi-source heterogeneous data sensing system, a rich and accurate data basis is provided for fault diagnosis, noise interference is reduced through a two-channel hybrid filtering architecture and a self-adaptive filtering parameter adjustment mechanism, and the fault diagnosis accuracy is improved. And a solid foundation is laid for subsequent feature extraction and model training.

The invention provides a disease screening system based on a large model. The disease screening system is used for solving the technical problem that an existing disease screening system is intelligently used for single special disease screening. The system comprises a scheduling model and a plurality of AI auxiliary diagnosis models, the scheduling model is connected with the plurality of AI auxiliary diagnosis models, and the scheduling model is connected with a big data disease library. According to the method, a high-level scheduling model is utilized, multiple single AI auxiliary diagnosis models are managed in a centralized mode, automatic calling of a multi-disease AI system is achieved, automatic structured report generation is achieved through a large language model, historical medical history is combined, progress is predicted, treatment suggestions and reference cases are automatically given, an existing clinician film reading workflow is fitted, and the efficiency is improved. The whole process of actual diagnosis decision making of doctors is greatly fitted, and the working efficiency of the doctors can be improved to the maximum extent.

The invention provides a radiotherapy patient skin injury real-time detection record management system based on image processing, and belongs to the technical field of radiotherapy patient skin injury real-time detection. Comprising a multispectral image acquisition module for imaging the skin of a patient at the same visual angle by adopting different spectrums to obtain three spectral images including near-infrared imaging, infrared imaging and ultraviolet imaging; a first identification module is used for fusing three spectral images to obtain an image data set, and a second identification module is used for constructing an epidermis injury coefficient # imgabs0 #, a dermis injury coefficient # imgabs1 # and a subcutaneous injury coefficient # imgabs2 # of the ith patient and evaluating the epidermis injury coefficient # imgabs0 #, the dermis injury coefficient # imgabs1 # and the subcutaneous injury coefficient # imgabs2 # respectively. A skin injury evaluation model is constructed, the comprehensive injury area ratio is calculated, the classification of injury levels is realized, data support is provided for a personalized treatment scheme, and a powerful auxiliary diagnosis tool is provided for a medical institution.

The invention relates to the technical field related to artificial intelligence, in particular to an intelligent medical diagnosis engine based on an AI large model, which comprises an auxiliary diagnosis module for collecting data such as an electronic medical record and a medical image of a patient and predicting an auxiliary result by using an integrated learning method; the drug recommendation module is used for analyzing drug recommendation data by using an AI large model and predicting potential curative effects and side effects of new drugs; and the operation simulation module is used for constructing an operation environment and a human body model in combination with a virtual reality technology. A large amount of medical data can be quickly processed and analyzed through auxiliary diagnosis, the diagnosis time is shortened, gene, physiological and pathological multi-omics data of a patient can be integrated by reducing unnecessary invasive examination and misdiagnosis and drug recommendation, a personalized drug treatment scheme is provided for the patient, and the patient experience is improved. Comprehensive and accurate diagnosis basis and treatment suggestions are provided for doctors; by accurately simulating a real operation scene including mechanical and optical characteristics, a more real training experience is provided for doctors and medical students.

The invention discloses a heterogeneous double-flow fusion method and system for diabetic retinopathy grading. The method comprises the following steps: obtaining an output result of diabetic retinopathy grading by utilizing a heterogeneous double-flow architecture; processing an input fundus image into images with different resolutions; extracting global context features from the low-resolution image by using a lightweight visual Transform model distilled by composite knowledge, and extracting local focus features from the high-resolution image by using a convolutional neural network model; performing interactive fusion on the global context features and the local focus features of the double-branch architecture through a symmetric bidirectional cross attention fusion module to obtain enhanced fusion feature representation; and finally, inputting the fusion features into a classifier, and outputting a severity grading result of the lesion. The method aims at improving the accuracy and robustness of hierarchical diagnosis through deep analysis of global information and local details, and can be applied to the medical fields of clinical computer-aided diagnosis, eye image analysis and the like.

The invention discloses a liver and gall lesion early screening system and method based on image fusion, and relates to the technical field of medical image processing and computer-aided diagnosis, and the method comprises the following steps: reconstructing a multi-modal image space-time coordinate system under a unified event time baseline, generating a respiratory displacement field and a magnetic sensitive pulse fingerprint, and constructing an artifact suspicion map; and performing anti-fact playback based on the artifact suspicion chart, performing frame-by-frame playback on the image acquisition sequence, quantifying artifact superposition tracks with consistent directions, and solidifying an artifact anchor point set. According to the method, space-time coordinates are constructed based on a unified event time baseline, a breathing displacement field and magnetic sensing pulse fingerprints are introduced, anti-fact playback, distortion kernel inference and residual decoupling are combined, artifact recognition and fusion intervention are achieved, and artifact closed-loop elimination is completed by judging threshold-driven fusion regulation and time reversal phase gating, so that the artifact recognition accuracy is improved. And the fused image authenticity is improved.

The invention relates to the technical field of medical artificial intelligence, in particular to a pulmonary nodule malignant risk dynamic prediction method and system based on space-time attention and multi-modal data guide fusion. Clinical features are obtained based on clinical data, and a lung CT slice image is obtained based on a lung CT image; the method comprises the following steps: extracting general medical visual features in an image by using an open-source medical large model, extracting image features in a lung CT slice image through a visual Transform model, and performing feature guide optimization on the image features based on clinical features and the general medical visual features to obtain patient image specific features; performing joint mapping on the specific features of the patient image by using a space-time attention mechanism to obtain space-time enhancement features of the patient image; and performing weighted fusion on the clinical features, the general medical visual features and the space-time enhancement features of the patient by using a gating mechanism, and obtaining the risk prediction probability of the malignant pulmonary nodules of the patient by using a classifier. The method can predict the malignant probability of the pulmonary nodule, and facilitates the realization of auxiliary diagnosis such as pulmonary nodule screening.

The invention discloses an Alzheimer's disease auxiliary screening system based on intelligent interaction, particularly relates to the field of computer-aided diagnosis, and is used for solving the problem that in an existing screening method, it is difficult to quantify cognitive performance differences of life knowledge and professional knowledge at the same time. The method comprises the following steps: constructing a task interaction group covering life knowledge and professional knowledge, collecting and coding multi-dimensional interaction operation of a testee in a task process, and generating an interaction vector matrix; further performing jump point identification, vector residual calculation and invalid operation density statistics on the interaction behavior through a preset task logic chain, and quantifying the operation deviation condition of the interaction behavior; respectively generating life and professional cognitive performance indexes based on the analysis result, and constructing a cognitive deviation evolution curve of a difference value of the life and professional cognitive performance indexes along with time change in a set period; and finally, through identifying a continuous rising trend in a cognitive deviation evolution process, auxiliary screening and early warning of asymmetric degradation of cognitive competence are realized.

The invention relates to the technical field of medical image processing and computer vision, in particular to a medical image segmentation method and system based on deep learning, the method is based on a U-shaped encoder-decoder architecture, a DSAB module is introduced into an encoder, and context perception of a directional anatomical structure is enhanced through complementary directional space shift and CSA mechanism weighting; an MGCF module is designed in a decoder, and a parallel multi-scale convolution path and an AGCA mechanism are combined, so that multi-level features are efficiently fused to recover boundary details. Meanwhile, links of data preprocessing, Transform structure details, segmentation result post-processing and the like are supplemented, the model performance is improved through a mixed loss function and an optimization training strategy, and the method has remarkable advantages in segmentation precision and boundary definition and provides powerful support for clinical auxiliary diagnosis.

The invention discloses a three-dimensional positron emission tomography data image classification method based on multi-stage progressive feature extraction, and is applied to the technical field of Alzheimer's disease auxiliary diagnosis. The auxiliary diagnosis method comprises the following steps: acquiring and preprocessing PET image data of an Alzheimer's disease patient; improving the reliability of the data set by using data enhancement; performing long-range dynamic modeling on the three-dimensional voxel sequence through a stacked Lmamba block; global context semantic adaptive fusion is realized through a layer-by-layer cross-scale channel attention fusion module (CSACF), and a channel spatial perception module (CSPM) is constructed to optimize spatial feature fusion; an inverted bottleneck module is mixed with long-distance space and position information to enhance the capturing capability of the model on detail features; and finally, predicting the disease category probability through global average pooling, full connection and softmax functions. According to the method, the precision of AD early diagnosis and MCI conversion risk prediction can be greatly improved, the defects of a medical image diagnosis method of a convolutional neural network (CNN) and Transform in long-range dependence on modeling and calculation complexity are overcome, and the method has good application prospects and is suitable for AD early detection and MCI conversion risk assessment.

The invention discloses a cervical cytopathy detection method based on a hypergraph convolutional network. The method comprises the following steps: performing sliding window slicing processing, unsupervised image decomposition and dyeing normalization on a cervical cytopathy image, generating normalized image input, and constructing an image input sample set; a target detection model is constructed based on YOLO11, a block-level feature extraction network (BBMM) module is embedded to enhance the perception ability, and a sparse attention module is adopted to perform key region feature enhancement; constructing a hypergraph neural network HGNN module based on a Patch-level relationship, and extracting a structural relationship between cells; integrating an uncertainty quantification mechanism, and generating a confidence thermodynamic diagram; a front-end and rear-end separated diagnosis platform is built, image uploading, detection result display, frame selection correction and interactive management are supported, and whole-process auxiliary diagnosis is achieved; the method has the advantages of high detection accuracy, high interpretability, flexible deployment and the like, and is suitable for intelligent early screening and clinical auxiliary diagnosis scenes of cervical cytopathy.

The invention discloses an intelligent auxiliary diagnosis system and method based on big data analysis, and relates to the field of intelligent auxiliary diagnosis. Original CT scanning data and electronic medical record data of a patient are processed in parallel, and a deep learning model is introduced to extract high-dimensional image features and text features; through constructing a knowledge prior relation matrix derived from clinical guidelines and expert rules, knowledge-guided cross-modal attention fusion is carried out on image features and text features so as to realize deep and accurate joint characterization of patient conditions, and then intelligent risk prediction and classification are carried out on the basis of the deep and accurate joint characterization. Thus, through deep coupling of data-driven feature learning and medical knowledge-driven logical reasoning, intelligent and logical integration of multi-modal medical information can be realized, and the accuracy and reliability of an auxiliary diagnosis model in a complex clinical scene are effectively improved.

The invention relates to the technical field of medical image intelligent analysis and artificial intelligence auxiliary diagnosis, in particular to a medical image disease course prediction system based on an industrial neural network, and the system comprises a reference generation module which is used for obtaining static image data; processing the static image data by using a physical perception neural network to generate a pure ideal state reference; a perturbation simulation module; the industrial kinetic parameters are used as perturbation terms to be superposed to a pure ideal state reference, and a theoretical damaged state is generated; the projection verification module is used for acquiring real multi-modal observation data; generating a real residual error; generating a theoretical residual error based on the theoretical damaged state and the pure ideal state reference; calculating a manifold coupling confidence coefficient; the closed-loop correction module is used for performing inversion optimization on the industrial kinetic parameters; outputting a disease course prediction result according to the manifold coupling confidence coefficient; according to the method, the problem that a traditional medical model lacks physical consistency explanation is solved, and the credibility of artificial intelligence auxiliary diagnosis is remarkably improved.

The invention belongs to the field of medical artificial intelligence, and particularly relates to a multi-modal data acquisition and fusion method for Alzheimer's disease. The method comprises the following steps: firstly, synchronously acquiring eye movement, expression, voice, gait and grip strength data of a subject through a virtual reality multi-task normal form, and combining with an MoCA scale to score a result; then preprocessing and feature extraction are carried out on each modal data, and unified feature representation is constructed; on the basis, a cross-modal attention mechanism is adopted to realize interaction and weighted fusion of multi-modal features, and a unified fusion feature vector table is generated; and finally, outputting structured data organized according to task fragments for auxiliary evaluation and modeling of cognitive impairment. The method can effectively solve the problems that in the prior art, single-mode information is insufficient, and multi-mode data are difficult to align and fuse, has the advantages of being low in cost, easy to popularize and high in detection accuracy, and can be widely applied to early recognition and auxiliary diagnosis of the Alzheimer's disease.

The invention discloses a B-ultrasonic auxiliary diagnosis system based on artificial intelligence. The B-ultrasonic auxiliary diagnosis system comprises a data acquisition and preprocessing module used for acquiring B-ultrasonic images and performing standardization processing; the physical characteristic extraction module is used for extracting physical characteristics of acoustic impedance, attenuation coefficients, echo intensity and scattering and reflection modes and providing embedded data for the feature fusion module; the feature fusion and representation module is used for deeply combining physical characteristics and spatial features through a multi-modal feature fusion and adaptive graph convolution method to generate high-dimensional feature representation; the deep learning diagnosis model module is used for realizing joint optimization of feature learning and diagnosis tasks on the basis of a multi-task deep neural network framework guided by physical semantics; and the visualization and auxiliary diagnosis module is used for presenting diagnosis results and providing decision support for doctors through thermodynamic diagrams, multi-modal analysis and intelligent explanation. According to the method, the limitation of a traditional deep learning model under the challenges of low contrast, noise interference, data shortage and the like can be solved.

The invention relates to the technical field of artificial intelligence auxiliary medical treatment, and provides an artificial intelligence-based marine first aid auxiliary diagnosis method and system. The method comprises the following steps: receiving a real-time multimedia data stream from an offshore first-aid site; carrying out conjoint analysis on the image and audio information, positioning key elements of the involved injured individuals and the surrounding environment, and obtaining an emergency scene description after enhanced analysis; extracting professional suggestions suitable for the current first-aid situation in combination with correction parameters of influences of specific variables of the marine environment on injuries and patients; activating a remote expert system, interacting with field first-aid personnel through two-way video communication, and generating personalized guidance information; and combining the personalized guidance information with the calibrated visual field of the first-aid personnel, and displaying decision support information provided by artificial intelligence in the visual field of the first-aid personnel. According to the technical scheme, the first-aid response speed and accuracy are improved, and it is ensured that the wounded patient obtains the most appropriate medical treatment in the first time.

The invention belongs to the technical field of intelligent diagnosis, and particularly relates to an AI combined MRI and clinical JIA diagnosis system and a storage medium. According to the system disclosed by the invention, the early auxiliary diagnosis of the juvenile idiopathic arthritis is carried out by combining artificial intelligence with multi-dimensional and multi-modal information of multi-sequence MRI images of knee joints of children and various clinical information. The main technology of the method is child knee joint tissue segmentation based on deep learning, a multi-dimensional feature extraction strategy based on a segmentation result, and disease classification based on multi-modal feature integration and deep learning. By integrating the multi-dimensional features of the multi-sequence MRI images and fusing different modal features such as image information and clinical information, an auxiliary diagnosis result with high accuracy can be provided. The technology provided by the invention is beneficial to the realization of early diagnosis and early treatment of juvenile idiopathic arthritis, and has a very good application prospect.

The invention provides an instance segmentation method and device for early intestinal cancer vessel infringement phenomenon analysis and a readable storage medium, and the method comprises the steps: constructing an end-to-end automatic process: firstly, carrying out the multi-magnification overlapping cutting of a digital pathological image, and carrying out the high-sensitivity instance segmentation through a Mask2form instance segmentation network; combining the overlapped masks by adopting an efficient union lookup algorithm, and recursively complementing incomplete instances in combination with an ROI (Region of Interest) expansion algorithm; hE, CD-31 and D2-40 dyeing full slice level alignment is realized through rigid-non-rigid-micro registration, and a three-mode instance level graph block is cut based on a registration result; and finally, fusing multi-modal features by using a hybrid expert network enhanced by a large pathological model, and dynamically screening expert paths to carry out true and false positive judgment. The invention provides an efficient technical scheme for early accurate diagnosis of intestinal cancer, and is suitable for pathological big data analysis and clinical auxiliary diagnosis.

The invention relates to the technical field of medical and multi-modal large models, and discloses a disease prediction and auxiliary diagnosis system construction method and system based on a multi-modal large model. The method comprises the steps of report format conversion, data cleaning and table image-to-structured text conversion. Constructing a retrieval knowledge base to enhance the retrieval capability; precise cue word design and reasoning optimization are carried out; small sample learning and model fine tuning; the invention discloses a multi-modal large model integration and visualization system. According to the system, the accuracy problem of a traditional disease risk prediction method is effectively solved, and a more reliable auxiliary diagnosis tool is provided.

The invention relates to the field of traditional Chinese medicine clinical auxiliary diagnosis, and discloses an acupuncture point recommendation method, an acupuncture point recommendation model obtaining method, an acupuncture point recommendation model obtaining device, acupuncture point recommendation equipment and a medium, and the acupuncture point recommendation model obtaining method comprises the following steps: collecting patient medical record data to form a medical record data sample set; preprocessing the medical record data sample set to obtain a standard medical record data sample set; inputting the standard medical record data sample set into a preset language model, performing preliminary training on the language model based on the standard medical record data sample set, so that the language model establishes a semantic mapping relationship between the patient symptom information and the acupuncture points, and performing parameter fine tuning and strategy optimization on the preliminarily trained language model in sequence, obtaining an acupuncture point recommendation model; according to the acupuncture point recommendation model obtaining method, the accuracy and individual adaptability of acupuncture clinical treatment are improved, the diagnosis and treatment efficiency is optimized, and a scientific and systematic treatment scheme is provided for a patient.

A method of providing vehicle diagnostics includes receiving a request for AI-assisted diagnostics, receiving diagnostic data obtained from a vehicle, prompting a user to describe one or more symptoms exhibited by the vehicle, processing the one or more symptoms and the diagnostic data at least in part using a natural language processing (NLP) model, and providing natural language guidance to the user based on a result of the processing. The natural language guidance may include testing instructions for the user to perform a step-by-step procedure to diagnose the vehicle and / or repair instructions for the user to perform a repair on the vehicle.

The invention discloses a cervical lesion intercellular relation modeling and analysis system based on a graph neural network, and the system comprises a medical image collection module which is used for collecting a digital image of a cervical tissue pathological section or a cervical TCT slide; the cell detection and segmentation module is used for extracting spatial position information and morphological characteristics of cells; the cell feature extraction module is used for extracting and fusing the spatial position, morphology, texture and biological marker features of the cells; the cell relation graph construction module is used for constructing a heterogeneous cell relation graph with cells as nodes and inter-cell relations as edges; the graph neural network analysis module is used for carrying out feature learning and modeling on the heterogeneous cell relation graph; the intelligent auxiliary diagnosis module is used for generating auxiliary diagnosis suggestions; and the data management and automatic control module is used for realizing automatic control and case data management of the whole process of the data. The intelligent and automatic level of cervical lesion cell analysis can be comprehensively improved, and the accuracy and efficiency of diagnosis are improved.

The invention discloses a newborn face feature rare disease auxiliary diagnosis system based on AI image recognition, relates to the technical field of rare disease auxiliary diagnosis, and aims to solve the technical problem that dynamic feature analysis is insufficient in existing newborn rare disease auxiliary diagnosis. Detection, segmentation, feature classification and dynamic change analysis are completed, and abnormal textures, pigment distribution or blood vessel features are identified; the face key point analysis module is used for marking face key anatomical points, calculating three-dimensional space coordinates and analyzing relative positions and geometrical relationships of the key points and evolution trends of the key points along with time; the dynamic modeling module is used for constructing a dynamic evolution model of the facial features of the newborns along with the change of day ages, and correlating the time dependence of growth and development stages and rare disease features; and the feature fusion unit is used for integrating output results of the skin analysis module, the face key point analysis module and the dynamic modeling module. The method has the advantage of improving the diagnosis accuracy.

The invention discloses an anomaly recognition method and system for multi-modal time sequence health data. The anomaly recognition method comprises the following steps of collecting physiological data, medical image data, text data, voice data and environment data of a user; carrying out cleaning, time alignment and standardization on the multi-modal data; the method comprises the following steps: respectively extracting each modal feature through LSTM (Long Short Term Memory), U-Net, ResNet, RoBERTa and EmotionNet; embedding the features into a unified feature space, adding position codes, and then inputting the features into a Transform model; performing inter-modal correlation modeling through a multi-layer coding stacking structure, and finally outputting a health state classification result through a full-connection network; the method supports anomaly detection under the condition of mode deficiency, has high robustness and expansibility, and is widely applied to health monitoring, medical auxiliary diagnosis and personalized health management scenes.