750 results about "Imaging study" patented technology

The invention discloses a benign and malignant nodule grading evaluation system based on large model fusion ultrasonic imaging and thyroid gene markers, which can organically fuse non-invasive examination and serological detection, can simulate and diagnose multi-grade risk probability information provided by a gold standard, realizes similar risk grading estimation in a non-invasive mode, and has a wide application prospect. The thyroid nodule risk assessment method can provide visual explanation conforming to clinical logic based on comprehensive information of iconography and molecular biology, can significantly improve the accuracy of thyroid nodule risk assessment, can also effectively improve clinical decision-making efficiency and patient credibility, and has important clinical application prospects. The system comprises a data acquisition module, an ultrasonic image feature extraction module, a gene marker feature extraction module, a multi-modal fusion and hierarchical reasoning module and a generation module.

The present invention relates to an early monitoring system for pancreatic cancer based on big data, including a data acquisition module, a feature extraction module, a risk assessment module and an early warning trigger module. The data acquisition module is used to obtain the user's laboratory test data, imaging test data and clinical symptom data. The feature extraction module is connected to the data acquisition module, and constructs a comprehensive feature vector by extracting biochemical indicator features, imaging features and symptom features. The risk assessment module uses a pre-trained deep learning model to analyze the feature vector and generate a pancreatic cancer risk probability. The early warning trigger module compares the risk probability with a preset threshold, generates an early warning signal when the risk probability exceeds the threshold, and pushes it to a medical service terminal to suggest further diagnosis. The present invention combines multimodal data and deep learning technology to achieve accurate early monitoring of pancreatic cancer and improve diagnostic efficiency and accuracy.

The invention provides a tumor type bone defect reconstruction method and system. The method comprises the following steps: acquiring bone defect information; constructing a bone defect restoration; constructing a bone defect three-dimensional model; and bone defect simulation surgery. According to the method, the bone defect information is obtained by evaluating the illness state of the patient, meanwhile, the anatomical structure of the bone tumor of the patient is subjected to structural evaluation through modern medical imaging, and the bone defect restoration is constructed in combination with the corresponding bone defect information; then, according to preset prosthesis design data, carrying out repair design on the bone defect prosthesis and obtaining three-dimensional skeleton data of a patient in real time, then, according to the obtained three-dimensional skeleton data, constructing a three-dimensional bone defect model and carrying out a bone defect simulation operation, and finally, predicting and optimizing the implantation position, direction and angle of the bone defect prosthesis. The effect of improving the construction accuracy of the bone defect prosthesis is achieved, and the problem that in the prior art, the construction accuracy of the bone defect prosthesis is low is solved.

The invention relates to the technical field of three-dimensional reconstruction, and provides a hepatic vessel three-dimensional reconstruction system based on image segmentation, which optimizes an encoder and a decoder of a U-Net by using an attention mechanism, a dynamic weighting uncertainty mechanism and an HS-Swish activation function in image segmentation processing. In combination with hierarchical attention enhancement jump connection and multi-scale cavity convolution, the liver image data is precisely processed; in the aspect of three-dimensional reconstruction, a classical Marching Cubes algorithm is improved, interlayer interpolation of an optical flow estimation method is optimized, intersection point calculation precision is improved by adopting a trisection method, and a high-quality three-dimensional reconstruction model is generated; the system effectively solves the problem that a traditional system is insufficient in precision, image segmentation and reconstruction effects are remarkably improved, and reliable support is provided for the field of liver iconography.

The invention belongs to the technical field of medical image processing, and particularly relates to an automatic positioning and typing method for cervical posterior longitudinal ligament ossification, which is characterized in that ossification focus spatial distribution characteristics of CT images and spinal cord morphological parameters of MRI images are synchronously analyzed based on CT and MRI images, a vertebral body-ossification-spinal cord spatial relationship is explicitly modeled through anatomical structure topological constraint, and a cervical vertebra-ossification-spinal cord spatial relationship is obtained. Diffuse calcification artifacts, heterogeneity signal interference and multi-modal data registration deviation are overcome, accurate positioning and typing diagnosis of the ossification are achieved, multi-dimensional feature fusion analysis is achieved, the limitation of traditional single iconography index diagnosis is broken through, space continuity and clinical parameters are integrated through a hybrid classifier, and the accuracy of diagnosis is improved. The accuracy and robustness of typing judgment are improved, a structured diagnosis report is automatically generated in the whole process, and the clinical decision-making efficiency and the standardization level are remarkably improved; the principle is scientific and reliable, and the ossification continuity index, the spinal canal invasion rate and the spinal cord compression grading parameters are automatically calculated according to the typing standard.

The invention relates to the technical field of brain surgery, in particular to a construction method based on a craniocerebral tumor minimally invasive surgery risk assessment model. The method comprises the following steps: acquiring craniocerebral tumor minimally invasive surgery clinical data, including patient iconography data, surgery record data, postoperative complication data and patient basic data; performing data cleaning on the surgical clinical data, and performing standardization processing to unify a data format so as to obtain a standardized clinical data set; and extracting the size, position and shape of the tumor and the relationship between the tumor and surrounding tissues from the standardized clinical data set, and generating a tumor space structure model by using a three-dimensional reconstruction technology. According to the method, main factors influencing the surgical risk are identified through a statistical analysis method, including the contribution degree of each feature to the surgical risk and the interaction between the features, and it is ensured that the evaluation process is more objective and accurate.

The invention belongs to the technical field of medical data processing and artificial intelligence, and particularly relates to a cardiovascular disease risk prediction system based on multi-modal fusion, which comprises a multi-modal data acquisition and preprocessing module, a cross-modal association graph construction module, a dynamic fusion and prediction module based on a graph neural network and an interpretability analysis module. By constructing a heterogeneous graph fusing prior knowledge and data driving and utilizing a graph attention network to perform multi-level dynamic feature fusion, deep integration and interaction of multi-modal data such as genomes, iconography, clinical and intestinal flora metabolism are realized, so that the accuracy and interpretability of cardiovascular disease risk prediction are improved.

The invention discloses a device for tracking a tumor position in real time in radiotherapy, and solves the problems of delay and inaccuracy in a traditional tumor position prediction method through a multi-model fusion method combining a deep learning model and a mathematical prediction model. Different from a traditional prediction method depending on an in-vitro marker or iconography technology, the tumor position is predicted in real time by adopting a long-short-term memory network, and the change of the tumor position along with respiratory movement is accurately captured by utilizing the strong time sequence modeling capability of the long-short-term memory network. By introducing the mathematical prediction model to compensate the future position of the tumor, the method can effectively solve the prediction error caused by system delay, and ensures that rays can accurately irradiate the tumor area in dynamic tumor tracking radiotherapy, thereby improving the accuracy and treatment effect of radiotherapy.

The invention discloses a rigidity self-adaptive intravascular stent based on a three-dimensional virtual woven structure and a control method thereof. The method comprises the steps of reconstructing a preoperative tumor blood vessel three-dimensional model based on clinical iconography data, constructing a stent initialization model, starting a hexagonal knitting three-dimensional virtual algorithm, performing intravascular stent knitting modeling, realizing structural parameter optimization and adaptive stent expansion in combination with preoperative image data of a patient, and completing personalized deployment. The wire winding angle and the mesh density of each area of the stent are adjustable, the stent is adaptive to the complex intracranial aneurysm blood vessel form, accurate fitting and risk guiding are achieved, the treatment effect is improved, the postoperative complication risk is reduced, and the stent has good engineering suitability and clinical application prospects.

The invention discloses an ovarian cancer preoperative accurate prediction system and method based on multi-modal imageomics characteristics, and the method comprises the steps: taking a US image, a CT image and an MRI image as the basis, carrying out the radiomics characteristic extraction of the three types of modal image data, and carrying out the combined screening of the three types of modal image data, so as to determine a matched classifier, a prediction task and a corresponding optimal characteristic combination; in this way, the multi-modal radiomics features, the classifier and prediction tasks of different levels are optimally matched, then preoperative accurate diagnosis of ovarian cancer pathology, staging and accurate prediction of PDS effects are achieved through optimal matching, and the prediction process is close to the clinical practice diagnosis and treatment thought; and iconography detection suggestions with the highest diagnostic value are provided for different diagnosis and treatment layers.

The invention provides a tumor detection data analysis method based on a peripheral blood immune marker, and belongs to the technical field of immunodetection.The tumor detection data analysis method comprises the steps that firstly, a peripheral blood sample of a patient is collected, and plasma and cell components are separated; analyzing various immune cell phenotypes by using flow cytometry; determining the concentration of key cell factors; analyzing the molecular expression level of the immune checkpoint; establishing an immune cell subset proportion empirical function; analyzing a cell factor expression mode by using an immune lineage transcriptome analysis model; establishing a tumor immune microenvironment score by combining the immune cell subset proportion and a model output result; the accuracy of the scoring system is verified through clinical follow-up visit and iconography examination; a standardized report template is formulated to provide a tumor early detection basis. Wherein the immune lineage transcriptome analytical model adopts a multi-modal attention fusion network architecture, three different immune data streams can be processed and integrated at the same time, and accurate evaluation of tumor-related immune states is realized.

The invention relates to the technical field of biomedicine, and discloses a rectal cancer postoperative recurrence risk prediction method based on a prediction model, and the method comprises the steps: obtaining a patient tumor in the in-vivo image data of a rectal cancer patient; extracting imaging features of the tumor of the patient, and identifying recurrence risk features of the rectal cancer patient from the imaging features; analyzing clinical pathological characteristics of the rectal cancer patient according to the preoperative evaluation data and the postoperative pathological report; key recurrence influence factors of the rectal cancer patients are identified, recurrence events and follow-up visit time of the rectal cancer patients are identified, risk scoring is carried out on the rectal cancer patients, and a risk scoring result is obtained; based on the risk scoring result, the recurrence risk stratification of the rectal cancer patient is defined, postoperative recurrence risk analysis is conducted on the rectal cancer patient in combination with the recurrence risk stratification, the recurrence risk characteristics and the risk scoring result, and a risk analysis result is obtained. According to the invention, the comprehensiveness and accuracy of the rectal cancer recurrence risk analysis result can be improved.

The invention discloses a multi-modal iconography evaluation method suitable for atrial fibrillation cardiac stroke. The method comprises the following steps: acquiring and preprocessing heart and brain multi-modal image data; carrying out multi-modal image registration on the preprocessed data; feature extraction and fusion are carried out on the registered heart and brain multi-mode image data; performing cross-modal feature alignment and fusion on the heart and brain image data; constructing a heart and cerebral vessel integrated evaluation interaction model based on a graph neural network; predicting the risk of the end-to-end cardiac stroke; according to the multi-modal iconography evaluation method, multi-modal heart image data and multi-modal brain image data are combined, a heart and cerebral vessel integrated evaluation framework is established, image features and clinical data are fused through an artificial intelligence method, and end-to-end stroke risk prediction and evaluation are achieved. According to the method, artificial intelligence and medical technologies are comprehensively utilized, and the accurate cardiac stroke assessment method is provided.

The invention relates to the technical field of medical information, in particular to an aortic dissection rescue guidance method and system based on multi-dimensional data fusion, the method comprises the following steps: acquiring comprehensive medical data of a patient, the comprehensive medical data comprising at least one of real-time vital sign data, iconography data and medical history information; performing fusion processing on the comprehensive medical data, constructing a disease condition portrait based on a fusion data set, capturing a disease condition change trend, and obtaining a dynamic disease condition portrait of the patient; constructing a rule base based on historical data, and matching the patient's condition portraits with rules in the rule base to obtain condition features and risk indexes; a risk assessment standard is established based on the illness state characteristics and the risk indexes, the illness state severity is assessed through the risk assessment standard, possible complications are predicted, and personalized rescue suggestions and decision support are provided for medical staff.

The invention belongs to the technical field of medical information, and particularly relates to an interactive system for improving doctor-patient communication experience based on augmented reality. The system comprises an acquisition module, a processing module and a putting module. The acquisition module comprises a depth sensing device, the depth sensing device is used for acquiring image information of a patient in a doctor-seeing environment, and the doctor-seeing environment comprises a plurality of reference objects of which the poses are fixed with the doctor-seeing environment; the data acquisition module is used for acquiring imaging data of a patient and corresponding diagnosis data; the processing module is used for constructing a three-dimensional model of the doctor-seeing environment as an environment model according to the poses of the reference objects in the doctor-seeing environment and the three-dimensional model of each reference object, and establishing an environment coordinate system according to the environment model, and the processing module further comprises a human body three-dimensional model; and the processing module is used for segmenting point cloud data of the reference object and the patient in the image information according to the reference object and the human body three-dimensional model. According to the scheme, the communication efficiency and effect between the medical staff and the patient in the medical environment are improved.

The invention discloses a medical data analysis method based on an intelligent agent, and the method comprises the following steps: S1, obtaining a multi-dimensional heterogeneous data source which comprises an unstructured log, a structured attribute tag, time sequence sensor data and an iconography data report; s2, performing structured processing on the dynamic rule base document; s3, performing multiple rounds of iterative reasoning by using the data analysis agent to generate a preliminary analysis decision scheme; s4, constructing a data set for training a process reward model PRM; s5, training a process reward model as a verifier of a data analysis agent reasoning process, and optimizing the process reward model by using a cross entropy loss function; and S6, optimizing a decision scheme generation process of the data analysis agent based on a process reward model, sampling M candidate states for each decision step by the agent in a step-by-step reasoning process, scoring each candidate state by using a verifier, and selecting the state with the highest score as a final decision of the step.

The invention belongs to the technical field of image processing, and provides an iconography priori knowledge-guided craniocerebral CT (Computed Tomography) various anomaly real-time detection method, which comprises the following steps of: acquiring a craniocerebral CT image sequence in real time; inputting the craniocerebral CT image sequence into a pre-trained anomaly detection model to obtain an anomaly detection result; the anomaly detection model comprises a backbone network used for extracting multi-scale features of a craniocerebral CT image sequence, the backbone network comprising a plurality of cascaded feature extraction modules, and at least one feature extraction module being a symmetric information attention module; a neck network and a multi-detection-head network; the symmetric information attention module comprises an opposite-side difference chart attention network or more than two cascaded opposite-side difference chart attention networks, a difference information acquisition unit and a feature enhancement unit. The invention also discloses a computer program product and an electronic equipment system. According to the method, the matching flexibility of the symmetrical anatomical structures on the two sides is improved, and the accuracy and reliability of craniocerebral CT image anomaly detection are improved.

The invention discloses a sacroiliac joint synthetic CT examination technology and system, and belongs to the technical field of medical image processing, and the technology comprises the steps: data input: employing an MRI image of an axial 3DT1 MGE sequence as input data, and guaranteeing that the image quality meets the subsequent processing requirements; in the deep learning model, a deep learning neural network model is designed and trained, the model can automatically learn the mapping relation between the MRI image and the CT image, and the MRI image is converted into a CT-like image with similar CT characteristics; image processing and optimization: through image enhancement, denoising and edge detection algorithms, the quality of the generated CT-like image is further optimized, and it is ensured that the CT-like image has high resolution and accuracy when displaying erosion, hardening and ankylosing lesions. The sacroiliac joint synthetic CT examination technology and the system thereof are a promising imaging evaluation method, the advantages of MRI and CT are combined, the diagnosis accuracy of sacroiliac joint lesions is improved, and meanwhile the risk that a patient receives radiation is reduced.

The invention discloses a liver transplantation post-operation intelligent management system and method based on multi-modal monitoring and AI. The method comprises the following steps that S1, physiological parameters, biomarker data, iconography data and self-reporting data of a patient are collected in real time; s2, carrying out preprocessing, feature extraction and fusion on the collected data, and predicting the postoperative complication risk of the patient by using a risk prediction model; s3, generating a personalized management strategy by using a reinforcement learning algorithm according to a risk prediction result, and providing early warning information and intervention suggestions for medical personnel and patients; s4, according to the postoperative management plan and the doctor's advice of the patient, a fixed-point medication reminding function is set, a personalized medication scheme is generated, and medication compliance of the patient is monitored; and S5, the patient makes an appointment for remote medical services, selects appropriate doctors and time to carry out video inquiry or online consultation, and records inquiry content.

The invention relates to the technical field of medical robot control, in particular to a teleoperation control method and system for an alimentary canal catheterization robot. The control method comprises the following steps: acquiring digestive tract three-dimensional structure data of a patient by utilizing imaging equipment; based on the three-dimensional structure data, establishing a patient individualized three-dimensional operation model through a teleoperation system; planning a catheterization path and an operation strategy according to the three-dimensional operation model; collecting motion state data of the alimentary canal catheterization robot in real time through a sensor of the teleoperation system; dynamically matching the motion state data acquired in real time with the three-dimensional operation model of the patient; deviation information fed back by a sensor in the operation process is obtained, an angle error signal is generated, and the catheter arranging robot is corrected in real time by adjusting a control strategy; the control system comprises a target sensor, a teleoperation part, a control module and an upper computer. By means of the mode, delay is reduced, high-precision control is provided, the force sensing technology is introduced, the feedback experience of an operator is enhanced, and therefore the precision and safety of surgical operation are improved.

The invention discloses an image recognition system for constructing osteoporotic vertebral fracture healing based on artificial intelligence, and particularly relates to the technical field of orthopedic treatment, which comprises a data preprocessing module for extracting and optimizing data from an original medical image. According to the method, the advantage of discipline crossing is developed, a recognition system on medical imaging is developed based on deep learning, the advantage that the recognition system can discover detail features and hidden rules which cannot be found by human beings is utilized, and then a large amount of structured data is utilized to automatically learn and train visual feature expression of abstract data, so that the clinical missed diagnosis rate is reduced, and the clinical diagnosis efficiency is improved. The artificial intelligence architecture is trained by a rigorous and scientific means, so that the artificial intelligence architecture has clinical experience and knowledge of high-age dominant doctors and is popularized in local hospitals to achieve an auxiliary diagnosis effect, the difference of medical differentiation is reduced, the technical and experience defects of local medical personnel are made up, and the technical and experience defects of the local medical personnel are overcome. The aim of improving the definite diagnosis rate of diseases and reducing the missed diagnosis rate is one of the purposes of the invention.

The invention relates to the technical field of hepatitis diagnosis and treatment management, and discloses a block chain-based hepatitis patient full-period management method. The method comprises the following steps: creating a patient identity chain in a block chain network, and storing patient registration data and initial diagnosis information; full-cycle diagnosis and treatment data of a patient are obtained and divided into clinical examination, iconography, medication records and life sign monitoring data. Performing multi-dimensional cleaning on the clinical examination data to generate a standardized data set; inputting the iconography data into a pre-trained hepatitis feature extraction model, and outputting a liver injury feature set; verifying the integrity of the medication record, and generating a non-tampering medication time sequence chain; comparing the life sign monitoring data with preset health reference parameters to generate a sign deviation coefficient set; according to the method, data safety and traceability are guaranteed through the block chain, diagnosis and treatment data quality is improved through multi-dimensional data processing, patient privacy is protected, and reliable support is provided for full-period management of hepatitis patients.

The invention relates to an alveolar bone surface tooth root detection method and detector, and the method comprises the following steps: obtaining age data and bone density data of a testee according to CT data and imaging data of the testee, and matching corresponding ultrasonic parameters and acoustic impedance correction parameters; constructing a multi-layer medium acoustic impedance model comprising a tooth root layer of the bone surface of the alveolar bone; configuring transmitted ultrasonic waves based on the ultrasonic wave parameters; receiving an echo signal corresponding to the transmitted ultrasonic wave, and performing adaptive correction on parameters of each layer in the multilayer medium acoustic impedance model based on the acoustic impedance correction parameters by taking minimization of the difference between the echo signal and a theoretical reflection signal obtained by the multilayer medium acoustic impedance model as a target; and based on the multi-layer medium acoustic impedance model after self-adaptive correction, realizing detection of the tooth root on the bone surface of the alveolar bone. Compared with the prior art, the method has the advantages of accurate detection of the tooth root on the bone surface of the alveolar bone, high adaptability and the like.

The invention relates to the technical field of image motion analysis, in particular to a rectal cancer radiotherapy effect dynamic monitoring system combined with iconography analysis. Determining a reference recovery index representing the recovery effect from the dimensions of edge change and area change according to the characteristic that the lesion area becomes smaller and smaller along with the better and better treatment effect; then, based on the reference recovery index and in combination with the characteristic that rectum tissue circularity can cause gradual inward contraction of a lesion area and the edge is smoother and smoother, determining a more accurate shape recovery degree representing the reference recovery index; therefore, the corrected radiotherapy recovery curve is corrected more accurately according to the shape recovery degree, and dynamic monitoring of the rectal cancer radiotherapy effect is more visual and accurate.

The invention relates to the technical field of medical equipment, and provides a multi-modal lymphedema assessment and surgical navigation system based on image processing, which comprises a 3D scanning modeling module used for acquiring three-dimensional point cloud data of the body surface of a patient through structured light 3D scanning equipment, an iconography examination analysis module used for performing U-Net image segmentation on CT / MRI image data, and an image processing module used for processing the CT / MRI image data. The ultrasonic result calculation module is used for carrying out Canny edge detection on an ultrasonic image to determine the boundary of a surgical site, and the data integration and navigation generation module is used for realizing multi-modal data fusion through mutual information maximization, generating a surgical path based on FMM and carrying out real-time navigation in combination with an AR technology. According to the method, objective evaluation of lymphedema and accurate navigation of the operation are realized through accurate integration of multi-modal data and an image processing technology, the diagnosis and treatment accuracy is effectively improved, the operation risk is reduced, complications are reduced, and a scientific basis is provided for personalized treatment.

The invention relates to the technical field of image recognition, in particular to a magnetic resonance spectrum imaging automatic positioning method and device, equipment and a medium, and the method comprises the steps: obtaining historical imaging data of different historical brain diseases; constructing a focus recognition module based on historical iconography data; scanning a transection position, a sagittal position and a coronal position of the brain of the target patient to obtain a scanning result; based on the scanning result and a focus recognition model, performing preliminary recognition on the scanning result, and determining the focus level and area of the brain of the target patient; a mode recognition method is adopted to recognize the focus level and area, and the focus parenchyma center is accurately determined; according to the method, scanning is carried out by taking the focus parenchyma center as the single voxel center and taking the focus parenchyma center and normal tissue positions around the focus area as multiple voxels, positioning of magnetic resonance spectrum imaging is realized, a machine model training mode is adopted, the focus positioning speed can be increased, and thus the accuracy of single voxel and multi-voxel spectrum imaging positioning is improved.

A computer-implemented method for estimating a vital sign of a subject comprises receiving a sequence of imaging photoplethysmography (iPPG) signals measured from different regions of a skin of the subject and executing a cross-domain unrolling optimization iteratively minimizing a difference between the received iPPG signals and reconstructed iPPG signals followed by processing the reconstructed iPPG signals with an iPPG neural network trained in the frequency domain with machine learning to enforce a learned structure on the frequency coefficients of the reconstructed iPPG signals. The method further comprises determining the vital sign signal of the subject from the frequency coefficients of the reconstructed iPPG signals upon reaching a termination condition of the cross-domain unrolling optimization and outputting the vital sign signal corresponding to the reconstructed iPPG signals.

The invention relates to the field of HICH auxiliary nursing systems, in particular to an HICH intelligent rehabilitation large health system based on image and text recognition multi-round complementation. According to the scheme, the system comprises an acquisition module used for acquiring electrophysiological data, iconography data and clinical text data of past treatment events of a patient, integrating the electrophysiological data, the iconography data and the clinical text data into training data and integrating the training data into first data; the graph construction module is used for recognizing the iconography data and the electrophysiological data as text data, performing character recognition and semantic extraction on the text data, determining an entity relationship through multi-party mapping of the extracted content, and establishing an HICH rehabilitation knowledge graph; the data processing module is also used for performing character recognition and semantic extraction on the clinical text data, and checking and complementing the HICH rehabilitation knowledge map by using the extracted content; and the decision module comprises a treatment weight unit and a Gaussian model unit. According to the scheme, the problem that the reliability of the HICH rehabilitation diagnosis and treatment result calculated through AI recognition is low is solved.

The invention belongs to the technical field of medical image intelligent diagnosis, and provides a breast cancer focus benign and malignant discrimination method based on a gated multi-expert mechanism. The method comprises the following steps: firstly, carrying out standardization and semantic preprocessing on a mammary gland X-ray image, a BI-RADS imaging report and structured clinical data, embedding age, mammary gland density and focus position information into a text template in a natural language form, and realizing unified expression of multi-modal input; secondly, extracting image features by utilizing a ResNet network and a simplified CLIP model, obtaining a text semantic vector by adopting a Bio-ClinicalBERT model, and establishing two sub-paths of a lump expert and a calcification expert in a Transform structure; further, an expert weight is dynamically generated through a gating routing mechanism, and soft routing fusion is executed; and finally, outputting benign and malignant results of the breast cancer focus by the binary classification module. According to the method, deep fusion and dynamic collaboration of the mammary gland X-ray image, the BI-RADS text and the clinical information are realized, and the accuracy and interpretability of breast cancer discrimination can be remarkably improved.

The invention relates to the technical field of medical data, in particular to an acute infection rapid diagnosis method, device and equipment, and the method comprises the steps: collecting an electronic medical record, blood routine, inflammation indexes, a microbiological detection result and imaging data of a target patient; respectively extracting corresponding target basic features based on the electronic medical record, the blood routine, the inflammation index, the microbiological detection result and the imaging data; based on the basic features, a convolutional neural network and a long-short-term memory network, target depth features are extracted; obtaining an acute infection prediction model, wherein the acute infection prediction model is obtained through model training; based on the target depth feature and the acute infection prediction model, the acute infection type of the target patient is determined, the accuracy and efficiency of diagnosis are further improved, scientific antibiotic use guidance is provided for doctors, waste of medical resources is reduced, and prognosis of the patient is improved.