871 results about "Cerebral stroke" patented technology

The invention provides an acute cerebral apoplexy early-stage intelligent screening and early warning method and system based on deep learning, and relates to the technical field of medical imaging and artificial intelligence, and the method comprises the steps: obtaining a multi-temporal angiography image of the brain of a patient through a pulse-type dynamic imaging scheme, extracting hemodynamic features through a double-flow feature extraction network, and obtaining a multi-temporal angiography image of the brain of the patient; and constructing a vasculopathy risk distribution diagram by combining a vascular structure analysis model, and marking a potential embolism risk area. Furthermore, in combination with tissue perfusion parameters obtained by multispectral imaging and brain tissue function parameters obtained by electroencephalogram signals, an ischemic penumbra region is identified by using a self-adaptive threshold segmentation model. And finally, based on the ischemic penumbra area, the tissue perfusion parameters and the brain tissue function parameters, calculating a risk level by using a hierarchical decision model with a space-time attention mechanism, and generating a corresponding clinical intervention scheme. According to the invention, the early accurate screening and risk early warning of acute cerebral apoplexy can be realized, and the clinical intervention efficiency is improved.

The invention belongs to the field of cerebral apoplexy rehabilitation training, and provides a cerebral apoplexy upper limb dynamic rehabilitation method and system based on vagus nerve electrical stimulation, the excitement degree of a cortical spinal cord pathway of a patient is measured and evaluated by using motor evoked potential to obtain a motor function evaluation result, and an initial rehabilitation training task is set according to the motor function evaluation result; the training motion intention of the patient is determined by monitoring the electroencephalogram signal and the electromyographic signal of the patient, and percutaneous ear vagus nerve electrical stimulation is triggered; after percutaneous ear vagus nerve electrical stimulation, performing rehabilitation training analysis on basic clinical characteristics and multi-modal signals of the patient to determine intermuscular coordination, brain region connectivity and brain muscle coupling of the patient; the training condition of the patient is evaluated according to the rehabilitation training analysis result, and parameters of percutaneous ear vagus nerve electrical stimulation and training tasks are adjusted based on the evaluation result for iterative rehabilitation training; and stopping iteration until the rehabilitation training analysis result of the patient reaches a preset standard, and completing rehabilitation training.

The invention relates to the technical field of intelligent medical systems, and discloses a cerebral apoplexy onset risk assessment and reminding method and system.The method comprises the steps that continuous medical structured detection data are collected, and the data comprise carotid artery blood flow parameters, brain oxygen saturation, heart rate variability and metabolic indexes; inputting a bidirectional LSTM, a differential convolutional network, a wavelet residual network and a multi-layer perceptron to extract nonlinear features; constructing a neural function coupling structure diagram of four nodes of cerebral blood supply, oxygen supply, autonomous regulation and metabolic steady state; calculating inter-node time sequence offset correlation and a stable factor to obtain a coupling anomaly coefficient; and driving the embedded network by using a graph structure and a node feature input mechanism, and outputting a risk state assessment result. According to the method, the neural function coupling structure diagram is constructed and the mechanism is introduced to drive the embedded network, so that high-precision identification of the multi-system collaborative abnormal state and dynamic evaluation of the stroke risk level are realized.

The invention discloses an intelligent assessment method and device for body rehabilitation of a stroke patient, and relates to the technical field of rehabilitation intelligent assessment, and the method comprises the steps: connecting a medical information management system, and obtaining the stroke medical record information of the current patient; performing identification according to the stroke medical record information; the multi-modal sensing module is connected with the rehabilitation device, performs rehabilitation motion sensing on the limb on the affected side according to the multi-modal sensor, and outputs a multi-modal sensing data set; collecting a multi-modal sensing sample set of a healthy limb corresponding to the limb on the affected side; a rehabilitation evaluation model is constructed, the rehabilitation evaluation model evaluates the multi-modal sensing data set, and rehabilitation level indexes are obtained; the rehabilitation level indexes are fed back to the rehabilitation device for training mode optimization. The technical problems that in the prior art, body rehabilitation evaluation depends on subjective judgment, the limb function recovery condition of the patient is difficult to comprehensively reflect, rehabilitation evaluation efficiency is poor, and the result is inaccurate are solved, and the technical effect of improving rehabilitation efficiency and evaluation accuracy is achieved.

The invention discloses a cerebral apoplexy risk and prognosis prediction system and method, and relates to the field of intelligent medical treatment, and the system comprises a data processing and knowledge construction layer which is used for extracting, cleaning and constructing a space-time multi-modal knowledge graph and structured clinical features from multi-source heterogeneous medical data; the feature engineering and fusion layer is used for deeply fusing dynamic semantic information in the space-time multi-modal knowledge graph and the structured clinical features through a graph embedding and attention mechanism to generate a fusion feature vector for a cerebral apoplexy prediction task; and the prediction model and output layer is used for performing cerebral apoplexy risk and prognosis prediction based on the fusion feature vector to obtain a prediction result, and generating a decision result for assisting a doctor in understanding the model through an interpretable mechanism. The method provided by the invention can improve the accuracy of stroke recurrence, bleeding transformation or function prognosis prediction, and provides a new way for accurate stroke management.

Disclosed in the present invention are a method and system for predicting a hematoma expansion event of a stroke patient. The method comprises: collecting patient data information, and performing data preprocessing; using an XG-Boost model to calculate the SHAP average value and the contribution degrees of different types of data, and extracting a feature quantity with the highest contribution degree from each type of data; performing multivariate linear regression analysis to obtain the weight of the effect of each type of data on hematoma expansion, and weighting all the feature quantities on the basis of the weight to obtain weighted feature quantities; using an extreme learning machine to predict a hematoma expansion probability; and using a northern goshawk optimization algorithm to search predicted values for the boundary of occurrence of a hematoma expansion event, and verifying the prediction accuracy. The present invention can accurately analyze and predict the expansion risk of hematoma after hemorrhagic stroke, which is crucial to early diagnosis and timely treatment.

The invention relates to a mechanical exoskeleton rehabilitation training system and method based on a brain-computer interface. The system comprises electroencephalogram acquisition equipment, a preprocessing unit, a feature extraction unit, a motion intention decoding unit, a mechanical exoskeleton control unit, a muscular tension state monitoring unit and a self-adaptive functional electrical stimulation feedback unit. The method comprises the following steps: preprocessing electroencephalogram and electromyographic signals; electroencephalogram and myoelectricity time-frequency features are obtained through feature extraction; an electroencephalogram decoding model is used for decoding to obtain the movement intention of the patient, and an exoskeleton control instruction is generated to control a mechanical exoskeleton control unit to drive the limb movement of the rehabilitation patient for rehabilitation training; meanwhile, the muscular tension state of the patient is analyzed according to the time-frequency characteristics of electroencephalogram and myoelectricity, functional electrical stimulation of different intensities is applied in a self-adaptive mode, stimulation feedback is enhanced, and the muscular tension state of the patient is adjusted. According to the invention, deep fusion of brain-controlled exoskeleton training and low-frequency nerve electrical stimulation adjustment can be realized, and the rehabilitation effect of a stroke patient is improved.

The invention discloses a rehabilitation assisting method and device suitable for cognitive disorder treatment and a medium, and relates to medical rehabilitation. A tablet personal computer, a millimeter wave radar and the like are included, artificial intelligence technologies such as signal processing and deep learning are utilized, and rehabilitation training of patients with mild cognitive impairment caused by brain injury and cerebral apoplexy is assisted. Basic information, medical history and current cognitive conditions of the patient are recorded and collected through the tablet computer, and a rehabilitation file is generated; based on the cognitive disorder type and severity of the patient, a rehabilitation training plan is intelligently made; a patient performs rehabilitation training through a gamified training interface, and the physiological state in the training process is monitored in real time in a non-contact mode based on millimeter wave radar; a deep learning model is used for analyzing training data of the patient, and rehabilitation progress is evaluated; training and evaluation data are summarized, and a subsequent rehabilitation scheme is optimized. Full-process automatic management from patient information collection, training plan making, training implementation, rehabilitation evaluation to data analysis report generation can be rapidly and accurately achieved, and the cognitive rehabilitation efficiency and effect are improved.

The invention belongs to the field of rehabilitation medical treatment and intelligent computing, and particularly discloses a cerebral apoplexy rehabilitation personalized treatment recommendation system based on artificial intelligence. Comprising a multi-modal data acquisition module, a feature extraction and clustering modeling module, a personalized path making module and a recommended path generation and feedback optimization module, and can realize dynamic monitoring and intelligent analysis of rehabilitation data of a stroke patient; a multi-modal aggregation neural network model is constructed by fusing multi-dimensional data such as images, biomarkers, clinical behaviors and cognitive emotions, a rehabilitation path is formulated according to a medical data clustering result, and a recommended path is dynamically adjusted and optimized in combination with treatment feedback of a patient; compared with a traditional method, the method has the advantages that scientificity and adaptability of personalized treatment are improved, the method has the advantages of real-time updating of rehabilitation paths, closed-loop management of a system, high model robustness and the like, rehabilitation intervention efficiency and accuracy can be remarkably improved, and technical support is provided for intelligent development of rehabilitation medicine.

The invention belongs to the technical field of cerebral apoplexy rehabilitation medicine, and relates to evaluation of myoelectric medicine, in particular to an HD-sEMG-based evaluation method and evaluation system for hand dyskinesia of a cerebral apoplexy hemiplegia patient. According to the invention, the high-density surface myoelectricity data of the front arms of the healthy side and the affected side can be respectively collected when the stroke hemiplegia patient executes different hand actions, and the microscopic neuron characteristics are analyzed, so that the degree and progress of dyskinesia of the stroke hemiplegia patient can be accurately evaluated. Through statistical analysis of the number of motion units, the average discharge rate and the discharge mode, changes and rules of neurons in the rehabilitation process are revealed, the difference between the healthy side and the affected side is compared, and the motion function condition of the neurons is evaluated.

The invention discloses a cerebral apoplexy multi-mode early screening intelligent evaluation system based on a large model, and relates to the technical field of medical health information, the cerebral apoplexy multi-mode early screening intelligent evaluation system comprises an intelligent management platform, and the intelligent management platform is in communication connection with the following modules: a multi-source heterogeneous data fusion engine, the data integration module is used for integrating multi-modal data including clinical data and terminal health data and constructing a health portrait of a patient; and the cerebral apoplexy knowledge graph construction platform is used for constructing a cerebral apoplexy domain knowledge graph in combination with evidence-based medical knowledge. By combining the digital twinning technology and the intelligent risk assessment engine, the influence of different intervention schemes on the cerebral apoplexy risk can be simulated, personalized intervention suggestions are generated, a patient is helped to reduce the cerebral apoplexy risk and change from passive prediction to active intervention, the patient is helped to take effective measures earlier, the health condition is improved, and the patient experience is improved. The occurrence of cerebral apoplexy is prevented, so that the disability rate and the death rate caused by cerebral apoplexy are reduced.

The invention provides a medicine for preventing and treating cerebral arterial thrombosis, and belongs to the technical field of neuromedicine. The medicine comprises salmon polypeptide as shown in SEQ ID NO.1 and a pharmaceutically acceptable carrier, and experimental results show that the salmon polypeptide has good safety, can effectively improve oxygen-glucose deprivation / reoxygenation injury of brain cells, and remarkably reduces neurological impairment and cerebral infarction of mice suffering from cerebral arterial thrombosis. The invention also discloses a pharmaceutical composition for treating cerebral arterial thrombosis. The pharmaceutical composition comprises the salmon polypeptide and edaravone. The composition shows a remarkable synergistic effect, compared with the single use of the salmon polypeptide or edaravone, the composition can more remarkably reduce neurological impairment and cerebral infarction, and the actual effect is obviously superior to the theoretical superposition effect. Therefore, the invention provides a novel and more effective treatment scheme for the treatment of cerebral arterial thrombosis.

The invention discloses a cerebral apoplexy prediction method and device based on a cerebrovascular image representation group, a medium and a product, and relates to the field of image processing, and the method comprises the steps: constructing a clinical database; according to the multi-modal image feature group data, the clinical feature data and the clinical cause typing of the patient, constructing a clinical cause typing prediction model; according to TOF-MRA, a cerebrovascular tissue segmentation model and voxel-based morphological analysis are adopted to determine cerebrovascular image features, and positioning diagnosis of a responsible blood vessel area is carried out according to the cerebrovascular image features; according to the cerebrovascular image features in the TOF-MRA, the SWI, the T1WI, the T2-FLAIR and the corresponding clinical feature data, a cerebral apoplexy recurrence risk prediction model is constructed; according to the application, the pathogenesis classification of the acute cerebral apoplexy patient can be quickly and accurately realized in a clinical scene, the responsible blood vessel can be accurately identified, and the recurrence risk prediction can be realized in the early stage of the disease.

The invention discloses a method and a system for evaluating movement of two lower limbs of a stroke patient, and relates to the technical field of movement function evaluation. The invention discloses a stroke patient double-lower-limb movement evaluation system which comprises a task guide module, a movement acquisition module, a movement scoring module, an interference evaluation module, a trend analysis module, a combination analysis module and a result output module. According to the method, the scoring model based on crowd category feature matching is constructed, exercise ability scoring is performed by adopting a machine learning algorithm or a fixed weighted combination, personalized evaluation is realized according to the group features of different patients, and the generalization ability and prediction accuracy of the scoring model are improved; group modeling is carried out by introducing features such as patient age, gender and motion level, and a scoring strategy is automatically switched according to the number of samples, so that the scoring mechanism also has reliability and clinical consistency in a family scene with limited data volume.

The invention discloses a cerebral apoplexy high risk group positioning evaluation system based on multi-modal data, and relates to the technical field of medical information science, the cerebral apoplexy high risk group positioning evaluation system comprises a cerebral apoplexy prevention and control management platform, and the cerebral apoplexy prevention and control management platform is in communication connection with the following modules: a multi-modal data acquisition and integration module, the multi-modal data collection module is used for collecting multi-modal data related to cerebral apoplexy from multiple channels and carrying out preprocessing operation on the collected multi-modal data. By integrating clinical data, image data, omics data and terminal health data, multi-dimensional information related to the cerebral apoplexy can be comprehensively captured, particularly, cerebral vessel digital twin is utilized to simulate hemodynamic characteristics, a plurality of data sources are fused in combination with a graph neural network, high-risk groups can be recognized more accurately, and the accuracy of cerebral apoplexy recognition is improved. The accuracy and reliability of risk prediction are remarkably improved, the problem of missing detection caused by dependence on a single data source in a traditional method is solved, and more powerful support is provided for early intervention.

The invention discloses a cerebral apoplexy patient upper limb rehabilitation evaluation method based on electroencephalogram and electromyographic signals. The method specifically comprises the following steps that signals are collected and preprocessed; electroencephalogram and electromyographic signal decomposition; performing multi-scale processing; calculating transmission spectrum entropy; and upper limb rehabilitation grade evaluation: mapping a fitting relationship between the MSTSE significance area value of each patient and a Brunnstrom evaluation scale based on a Spearman correlation coefficient, analyzing an FCMC characteristic index and a change trend which are significantly related to the grade of the Brunnstrom evaluation scale, and performing quantitative evaluation on the disease grade of the stroke patient. According to the technical scheme, a binary variational mode decomposition-multi-scale transfer spectrum entropy method is used for representing the FCMC coupling relation between the brain motor cortex and the corresponding muscular tissue, and in combination with a Brunnstrom rating scale, objective quantitative evaluation of the rehabilitation level of the upper limbs of the stroke patient is achieved.

The invention relates to the technical field of medical data analysis and rehabilitation, in particular to a cerebral apoplexy patient rehabilitation exercise analysis system based on medical big data, which comprises a data acquisition module used for acquiring electromyographic signals, exercise data and behavior data of a patient to form a data set; the data processing and fusion module is used for preprocessing the electromyographic signals in the data set, normalizing motion and behavior data and fusing the motion and behavior data into a multi-modal feature vector; the hierarchical analysis module is used for performing motion intention recognition according to the preprocessed electromyographic signals and outputting an intention classification result, recognizing the motion intention and predicting a rehabilitation effect through a double-flow Transform encoder, and evaluating a behavior risk level in combination with a risk factor formula; the optimization module is used for dynamically adjusting the mirror image treatment intensity according to the risk level and optimizing the prediction model by adopting incremental learning; and the interaction module is used for generating a thermodynamic diagram superposition rehabilitation curve in the X-Y coordinate system, and marking doctor intervention suggestions to complete visual interaction.

The invention relates to the technical field of medical equipment, provides a non-intrusive closed-loop ear vagus nerve stimulator for improving cerebral apoplexy and a use method, and aims to improve the symptoms of a patient by stimulating the ear vagus nerve in a conchae area. The device integrates an electrical stimulation electrode, a host and a closed-loop feedback module, monitors brain blood flow changes and other physiological parameters of a patient in real time, and automatically adjusts stimulation parameters through an intelligent algorithm to realize personalized treatment. A microprocessor, a multi-mode monitoring unit and a data fusion analysis unit are arranged in the host, and accurate and efficient treatment is ensured. In addition, the equipment supports remote intelligent monitoring and guidance, a treatment plan can be remotely adjusted, and the treatment effect is improved. A user interaction interface is friendly, and operation of patients and medical staff is facilitated. The stimulator is light in design, the conductive silica gel electrode is adopted, through real-time monitoring and intelligent adjustment, accurate and efficient treatment is provided for a patient, the comfort level and treatment convenience of the patient are ensured, and a safe and effective rehabilitation scheme is provided for the stroke patient.

The invention relates to a cerebral apoplexy patient gait analysis and personalized rehabilitation correction method and system based on a big data model, and relates to the technical field of medical rehabilitation, a patient feature data set is generated by integrating multi-modal data including gaits, electroencephalogram, myoelectricity and the like, a rehabilitation critical period is determined by using real-time nerve regulation and control and brain-computer interface data, and the rehabilitation accuracy is improved. The system constructs a personalized rehabilitation scheme according to the optimized feature data set and rehabilitation training parameters, the rehabilitation effect is tracked through a rehabilitation progress evaluation module, the scheme relates to data fusion, dimension reduction, correlation analysis and dynamic adjustment of the rehabilitation scheme, the personalization and effectiveness of rehabilitation training are ensured, and the rehabilitation effect is improved. The invention aims to improve the rehabilitation efficiency and life quality of stroke patients.

The invention relates to an assessment method and system for a stroke recovery stage, and belongs to the technical field of medical assessment, the method comprises the following steps: obtaining demographic information of a stroke patient, and obtaining motion video data of the stroke patient, the motion video data comprising upper limb motion data, lower limb motion data and / or hand motion data; key points are extracted from the motion video data of each type based on a key point recognition algorithm, corresponding motion posture data in the motion video data of each type are calculated based on the positions of the key points, and the motion posture data comprise angle data and displacement data; and determining preset motion attitude data based on a Brunnstrom six-stage theory and demographic information, comparing the motion attitude data with the preset motion attitude data, and obtaining a recovery stage of the stroke patient based on a comparison result. Real-time monitoring and intelligent evaluation of the rehabilitation process of the stroke patient are realized, and the evaluation accuracy and diagnosis efficiency are improved.

The invention provides a fall detection algorithm model for a wearable device of a stroke patient, and relates to the technical field of health monitoring and posture detection, and the technical key points are as follows: a construction method of the model is as follows: S1: obtaining posture information of a wearer by using an inertial sensor IMU in the wearable device, after data collection is completed, performing low-pass filtering processing on original data to obtain sensor data; s2, decomposing the inertial sensor data obtained at the t moment into a trend component, a season component and a residual component by using LOESS according to an STL (Standard Template Library) cyclic trend decomposition method, and then carrying out normalization processing on the data; and S3, performing time sequence modeling on each inertial sensor component by using LSTM (Long Short Term Memory). According to the fall detection algorithm model for the wearable device of the stroke patient, collaborative optimization of multi-physical-quantity coupling feature decoupling and time sequence dynamic modeling is realized, and medical-level fall monitoring performance is realized under resource constraints of the wearable device.

The invention discloses an intelligent generation and maintenance system and method for a dynamic cerebral apoplexy knowledge graph, and relates to the field of intelligent medical treatment, and the system comprises a data access and preprocessing module which is used for accessing and retrieving cerebral apoplexy related information from a multi-source data source; the knowledge extraction module based on LLM is used for acquiring candidate knowledge fragment streams; the knowledge graph structuring and filling module is used for integrating the candidate knowledge fragment flow into a formal knowledge graph structure stored in a graph database; the LLM-driven verification and refinement module is used for detecting the candidate knowledge fragment stream and the formal knowledge graph structure to obtain a detection result; and the dynamic updating and maintaining module is used for realizing dynamic fusion and version control of the formal knowledge graph structure. Through modular architecture design, a high-performance knowledge extraction and verification mechanism of a large language model and a dynamic processing capability oriented to continuous updating are fused, and high automation, standardization and intelligentization of a medical knowledge graph construction process are realized.

The present invention provides an early intelligent screening and warning method and system for acute stroke based on deep learning, which relates to the field of medical imaging and artificial intelligence technology, including obtaining multi-phase angiography images of the patient's brain through a pulsed dynamic imaging scheme, extracting hemodynamic characteristics using a dual-stream feature extraction network, and constructing a vascular lesion risk distribution map in combination with a vascular structure analysis model to mark potential embolism risk areas. Further, combining tissue perfusion parameters obtained by multispectral imaging and brain tissue function parameters obtained by electroencephalogram signals, an adaptive threshold segmentation model is used to identify the ischemic penumbra area. Finally, based on the ischemic penumbra area, tissue perfusion parameters, and brain tissue function parameters, a hierarchical decision model with a spatiotemporal attention mechanism is used to calculate the risk level and generate a corresponding clinical intervention plan. The present invention can achieve early and accurate screening and risk warning of acute stroke, and improve the efficiency of clinical intervention.

The invention relates to the technical field of health management, in particular to a risk assessment method and device for a stroke patient, equipment and a medium, and the method comprises the steps: obtaining a video image and audio data of the stroke patient, the video image comprises a face reaction image made by the stroke patient based on the first guide instruction and a limb reaction image made by the stroke patient based on the second guide instruction, and the audio data is a reply made by the stroke patient based on the third guide instruction; determining a facial paralysis recognition result and an eye response recognition result based on the facial response image; determining a limb motion recognition result based on the limb reaction image; based on the audio data, determining a language pronunciation recognition result; according to the method, the risk assessment level of the stroke patient is determined based on the facial paralysis recognition result, the eye response recognition result, the limb movement recognition result and the language composition recognition result, and the risk of the stroke patient can be accurately and quickly assessed without considering medical environment conditions.

The invention relates to the technical field of intelligent medical auxiliary diagnosis and neural function automatic evaluation, in particular to an NIHSS score automatic evaluation system for a stroke patient. Comprising a multi-mode induction and perception unit which is used as a front-end data entry and is used for collecting patient response in real time to generate a video stream containing depth and color information and a synchronous audio stream; the dynamic reference calibration unit is used for extracting kinematic characteristics to construct an individualized nerve reference template; the neural motion spectrum decomposition unit is used for generating a spectrum pathological feature vector for distinguishing myasthenia and ataxia; the opposite-side image rejection analysis unit is used for generating compensation and driving confidence for representing a real nerve driving intention; the cross-modal reflection analysis unit is used for generating a sensory pathway integrity index according to the nerve conduction velocity difference; and the collaborative scoring decision engine is used for mapping the multi-modal features into standardized NIHSS scores. According to the method, the interference of age and basic physique on scoring is effectively eliminated, and a high-precision comparison reference can be provided for subsequent abnormal judgment of the affected side.

The invention provides a construction method and device of a cerebral apoplexy early diagnosis model, electronic equipment and a storage medium, which are applied to the field of model construction, and in a transformer training process, channel reduction or channel expansion is performed on a feedforward network based on an activation matrix and a current width of the feedforward network of transformer to adjust the width of the feedforward network, so that the accuracy of the cerebral apoplexy early diagnosis model is improved. On the premise of not sacrificing the model performance, the parameter quantity and video memory occupation are reduced, the computing resource overhead of a cerebral apoplexy early diagnosis system is reduced, and the adaptability is enhanced.

The invention discloses a cerebral apoplexy focus detection method and system, and belongs to the technical field of medical image detection. Extracting a fusion feature map of the brain image; for each region type, obtaining a representative feature which has the highest similarity with the feature at each pixel point position in the fused feature map in the class prototype set and carries a corresponding region type label, and further determining the region type to which each pixel point position in the fused feature map belongs so as to obtain a corresponding pseudo-label map; the class prototype set comprises representative features of different region types and is obtained in the training process of the system, and feature distribution of different region types in the memory bank is calculated through a Gaussian mixture model; for each region type, sampling is carried out based on feature distribution of the region type, and a plurality of representative features are obtained; the prototype-like set in the cerebral apoplexy detection method has real global context perception ability, can clearly distinguish the focus and various complex background structures, and can accurately realize cerebral apoplexy detection.

The embodiment of the invention provides a cerebral apoplexy focus detection and scoring method and device based on a multi-modal medical image. The method comprises the following steps: acquiring medical images of various different modals of a suspected acute ischemic cerebral apoplexy patient; performing registration fusion and focus labeling on the medical images of different modalities based on an attention mechanism to obtain a labeled first modal image; training a target detection model of a YOLOv8 framework based on Swin-Transform improvement by using the labeled first modal image, and carrying out target detection on the to-be-detected image based on the trained target detection model to obtain an infarction region detection result; training a semantic segmentation model by using the labeled ASPECT scoring and partitioning brain atlas, performing scoring region segmentation on the to-be-detected image based on the trained semantic segmentation model, and outputting the ASPECT scoring and partitioning brain atlas; and mapping the infarct area detection result to the ASPECT score partition brain map, and calculating to obtain an ASPECT score.

The invention discloses a curcumin-loaded brain-targeted drug delivery system as well as a preparation method and application thereof. The delivery system comprises a carrier macrophage and a curcumin drug, the curcumin nano-drug is prepared by encapsulating curcumin with a lipoic acid modified cyclodextrin conjugate, then macrophages and the curcumin nano-drug are mixed, and co-incubation is carried out, so that the curcumin nano-drug is constructed. The brain-targeted macrophage drug delivery system loaded with curcumin is a multifunctional composite drug with brain targeting property for treatment of cerebral arterial thrombosis. A brain-targeted macrophage drug delivery system loaded with the curcumin nano-drug gathers in cerebral apoplexy tissues, phenotypic transformation of inflammatory microglial cells is promoted, and a pro-inflammatory microenvironment is improved; the oxidative stress pressure of the stroke part is improved; the two effects promote each other, the stroke microenvironment is improved, the functions of neuronal cells are saved, the synergistic nerve protection effect can be achieved in the aspect of treating ischemic cerebral apoplexy, and potential safety hazards are reduced.

The invention belongs to the technical field of assessment of dysphagia of stroke patients, and provides a stroke patient dysphagia risk prediction and personalized intervention recommendation system based on R language construction, which comprises a data processing module, a prediction model module, an intervention recommendation module and an output module, the data processing module receives and processes an input predictive factor value, the predictive model module constructed based on a large amount of case data is used for calculating a dysphagia risk score, then the intervention recommendation module intelligently recommends personalized intervention measures, and the personalized intervention measures are displayed to a user in a visual form through the output module; according to the system, accurate prediction and personalized intervention recommendation of the dysphagia risk of the stroke patient are realized, the prediction accuracy and reliability are improved, meanwhile, the pertinence and effectiveness of intervention measures are enhanced, and the life quality and rehabilitation effect of the patient are improved.