389 results about "Cerebral part" patented technology

The invention relates to a brain function evaluation system for a patient with disturbance of consciousness based on a neural multi-modal monitoring technology, and belongs to the technical field of biomedicine, and the system comprises a multi-modal data acquisition module which is used for acquiring multi-modal data of an evaluated person, the multi-modal data comprises clinical behavioral data, cerebral blood flow data, brain image data and neural electrophysiological data, the neural electrophysiological data comprises electroencephalogram and evoked potential data, and the brain image data comprises brain structure images and brain function images; the multi-modal data evaluation module is used for evaluating the clinical behavioral data, the cerebral blood flow data, the brain image data and the neural electrophysiological data to obtain an evaluation value of each modal data; and the comprehensive evaluation module is used for processing the plurality of evaluation values by using a disturbance of consciousness awakening prediction model to obtain an evaluation result. The method has the effects that the brain function of the patient with the disturbance of consciousness is evaluated by using a multi-mode neural function monitoring technology, and the evaluation accuracy is improved.

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.

A histotripsy therapy system configured for the treatment of brain tissue is provided, which may include any number of features. In one embodiment, the system includes an ultrasound therapy transducer, a drainage catheter, and a plurality of piezoelectric sensors disposed in the drainage catheter. The ultrasound therapy is configured to transmit ultrasound pulses into the brain to generate cavitation that liquefies a target tissue in the brain. The drainage catheter is configured to detect the ultrasound pulses. An aberration correction algorithm can be executed by the system based on the ultrasound pulses measured by the drainage catheter to automatically correct for an aberration effect caused by the ultrasound pulses passing through a skullcap of the patient.

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 discloses a brain lesion nuclear magnetic image Transform diffusion segmentation method and a brain lesion nuclear magnetic image Transform diffusion segmentation system based on symmetry prior. The invention provides a novel prior-guided diffusion denoising brain lesion segmentation Transform model, long-distance coding features are extracted from an image, local information related to medical image prior features is supplemented, and frequency domain screening fusion coding components are further introduced. A U-shaped denoising model is designed based on a transform network structure, and in order to maintain accurate lesion-related semantic details, a prior feature coding branch is introduced. A priori feature coding branch considers modal-related voxel distribution characteristics and spatial symmetry in a modal, so that a priori guidance attention mask is learned, and attention weights of a pixel level and a patch level are dynamically adjusted. In order to eliminate image noise and a low contrast effect, in a decoding part, a three-branch wavelet frequency spectrum mixed attention module is designed for fusing and screening frequency spectrum feature embedding, and a stable segmentation result is further generated.

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 discloses a multi-scale brain tumor segmentation method and system based on adaptive KAN, and a storage medium. The method comprises the following steps: preprocessing three-dimensional brain magnetic resonance imaging data; the preprocessed data are input into an encoder, the encoder comprises a plurality of levels, and each level executes convolution operation to extract local features, executes spatial KAN processing to extract spatial features and downsamples a feature map; the output of the encoder is input into a bottleneck layer, and the bottleneck layer captures a multi-scale global context by using a plurality of parallel expansion convolution branches; the output of the bottleneck layer is input into a decoder, the decoder comprises a plurality of stages, and each stage executes transposing a convolution up-sampling feature map, executes cross-scale gating processing to fuse encoder jump connection features and decoder features, and executes spatial KAN processing to optimize features; and the output of the decoder is input into the output module. According to the method, the problems of low calculation efficiency, poor tumor heterogeneity adaptation, insufficient multi-scale context capture and the like in the existing brain tumor segmentation can be effectively solved.

The invention discloses a hidden multi-mode brain state real-time monitoring and closed-loop intervention system, and relates to the technical field of intelligent wearable equipment. The system comprises a cap body, and further comprises a multi-point electrode array used for collecting EEG (electroencephalogram) signals, EOG (eye movement) signals and EMG (electromyography) signals of a human body; the brain blood oxygen monitoring module is used for monitoring brain blood oxygen saturation; the signal receiving and processing module is used for preprocessing and analyzing data acquired by the multi-point electrode array and the brain blood oxygen monitoring module; the multi-mode stimulation module is used for implementing sensory and nerve stimulation on the user according to a judgment result of the state judgment module so as to intervene in a drowsiness or attention decline state; and the power supply module and the communication unit are used for providing a working power supply for the modules needing power supply in the system and carrying out data communication with external terminal equipment. The system can monitor the waking degree of the user with high precision and carry out closed-loop feedback intervention by various stimulation means.

The guiding device comprises a shell, a driving assembly is installed on the shell, the driving assembly comprises a rotary knob and a lifting block, the rotary knob is rotationally installed on the upper portion of the shell, and the rotary knob and the lifting block are connected in a threaded fit mode; a sliding rail is arranged on the side portion of the lifting block, a protruding part matched with the sliding rail is arranged on the inner wall of the shell, and the rotary knob is used for controlling the lifting block to slide in the shell. An injection assembly is installed on the lifting block and comprises a needle tube and a push needle used for blocking the needle tube, the needle tube is fixedly installed on the lower portion of the lifting block, and the push needle is connected with the needle tube in an inserted mode; a guide arm used for supporting the shell is installed on the shell. The device has the beneficial effects that the needle tube can uniformly move up and down through threaded fit of the knob and the lifting block, and the difficulty that medicine forms a medicine column on the brain of a patient is reduced; the push needle can block the needle tube to prevent brain tissues from being embedded into the needle tube; the scales on the device can enable the propelling distance of the needle tube to be accurately and clearly presented to a doctor.

The invention relates to the technical field of internet big data, in particular to a cerebral hemorrhage space positioning and prognosis prediction network system based on three-dimensional point cloud. The cerebral hemorrhage space positioning system comprises an image acquisition module used for acquiring a brain CT image to be processed; the offset correction module is used for performing offset correction on the brain CT image to obtain a corrected brain CT image; the hematoma region segmentation model is used for segmenting the corrected brain CT image to obtain a hematoma region; the point cloud calculation module is used for calculating three-dimensional space coordinates of hematoma voxels in a hematoma area in a brain coordinate system based on the corrected brain CT image and taking the three-dimensional space coordinates as point cloud data of the hematoma area; and the point cloud data output module is used for performing normalization processing on the point cloud data of the hematoma area to obtain hematoma three-dimensional point cloud data representing the hematoma three-dimensional space position. According to the method, an accurate and comprehensive data basis is provided for cerebral hemorrhage prognosis prediction, and the accuracy and comprehensiveness of subsequent cerebral hemorrhage prognosis prediction are improved.

The invention relates to the technical field of medical image auxiliary diagnosis, in particular to a nuclear magnetic resonance image auxiliary diagnosis method and system based on multi-modal fusion. The method comprises the following steps: step 1, synchronously acquiring a three-dimensional T1 weighted structure image, a T2 weighted fluid attenuation inversion recovery image and diffusion weighted imaging data of a subject, carrying out spatial registration by taking the T1 weighted image as a reference, and executing skull stripping and gray scale standardization; 2, individualized brain region segmentation is carried out based on a brain anatomical map, the lesion sensitivity weight of each modal is calculated for each segmented brain region, and the weight is obtained by quantifying the following parameters; step 3, extracting multi-modal image features in each brain region; and 4, inputting the fusion features of the whole brain region into a multi-task classifier. The standardization and alignment of the multi-mode MRI image in the space and gray level are realized, and the problems of space mismatch and feature interference among different modes are effectively solved.

The invention discloses a cognitive impairment screening method and device based on a VR eye tracker and near-infrared brain imaging. The device comprises a virtual reality scene generation module, an eye tracker data acquisition module, a near-infrared brain imaging module, a data evaluation processing module and a cognitive impairment screening module. The eye tracker data acquisition module is used for acquiring an eye movement data information set of a user in a virtual reality scene in real time; the eye movement data information set comprises a fixation point information sequence, a pupil area information sequence and an eye hopping frequency information sequence; the near-infrared brain imaging module is used for acquiring a blood oxygen information set of cerebral cortex when a user completes a cognitive task; the blood oxygen information set of the cerebral cortex comprises a brain blood oxygen information sequence; the data evaluation processing module is used for carrying out evaluation processing on the collected eye movement data information set and the blood oxygen information set of cerebral cortex to obtain cognitive disorder information of the user; the cognitive impairment information comprises information about whether cognitive impairment exists or not and the degree of the cognitive impairment.

The invention provides an eye control interaction method, device and system based on eye movement tracking, and relates to the technical field of data processing. The method comprises the following steps: collecting eye movement data and user feature data of a testee; the user feature data comprises age, pupil color and eye disease type; extracting eye movement feature data according to the eye movement data and the user feature data; generating brain activity state information of the testee according to the eye movement feature data, and determining an eye control instruction of the testee according to the brain activity state information, the eye movement feature data and the user feature data; and performing eye control interaction based on the eye control instruction. According to the method, the characteristics of brain activity states, eye diseases and the like possibly existing in different types of users can be considered, the user feature data are collected, feature extraction is carried out in combination with the eye movement data, and the eye movement feature data accurately reflecting the real eye movement intention of the user is obtained, so that the obtained eye control instruction is adaptive to different users; while the eye control interaction accuracy is improved, smoother and more natural interaction experience is brought to the user.

The invention relates to the technical field of medical instruments, in particular to a sleep monitoring system based on a non-invasive brain oxygen saturation monitor, which comprises a monitoring system and is used for synchronously and dynamically monitoring local brain oxygen in real time, and the monitoring system comprises a non-invasive brain oxygen dynamic monitoring unit used for monitoring the brain blood oxygen saturation of a patient in real time by adopting a near infrared spectrum technology; the multi-parameter synchronous analysis unit is used for integrating the brain oxygen data, the electroencephalogram signals, the cerebral blood flow data and the respiratory event indexes; the intelligent risk assessment engine analyzes a brain oxygen change rule based on a deep learning algorithm, and predicts a brain function impairment risk caused by sleep breathing disorder; in the intelligent risk assessment engine, on the basis of a space-time convolutional neural network, brain oxygen time sequence data and spatial distribution characteristics are combined, and a brain hypoxia mode of the OSA patient is recognized; the method has the characteristic of realizing early warning of the brain injury related to sleep breathing disorder through noninvasive brain oxygen monitoring, multi-parameter fusion analysis and intelligent risk assessment.

The invention discloses a blood volume detection device and method based on a brain angiography image, and relates to the technical field of blood volume detection of the brain angiography image, the blood volume detection device comprises a detection device body and a substrate, the top surface of the substrate is fixedly provided with a lifting platform, and the surface of the top end of the lifting platform is fixedly provided with a workbench; and the linear motor is installed in the middle of the top face of the workbench, and a movable bed plate is horizontally and fixedly installed on the movable end face of the top of the linear motor. According to the blood volume detection device and method based on the brain angiography image, displacement compensation closed-loop control is achieved through the fine adjustment feedback module of the adjusting mechanism, and the gear meshing clearance compensation error is reduced; the LSTM network and the hybrid FEM-LBM algorithm are cooperatively optimized, the blood flow calculation speed is multiplied, an isolation telescopic sleeve shell and a shielding plate are arranged to form a composite shielding layer, the radiation leakage amount is reduced, the radiation monitoring module achieves real-time alarm of dose exceeding, and the protection level is improved.

The invention belongs to the technical field of damage assessment, and particularly relates to a construction method of a rat traumatic brain injury model caused by explosive shock waves. By protecting other important visceral organs except the brain, interference of damage of visceral organs such as the lung to brain injury is avoided, the overall function stability of the body is maintained, the caused brain injury can more truly reflect the characteristics of explosive traumatic brain injury, and the credibility and repeatability of experimental data are improved. The construction method simulates loading of explosion shock wave parameters in a real environment, constructs an animal traumatic brain injury model through live ammunition explosion, is closer to an actual combat environment, and can truly reflect physical parameters related to explosion and a complex action process generated by interaction of the physical parameters. And a support is provided for developing molecular mechanisms of explosive traumatic brain injury, clinical diagnosis, screening of therapeutic drugs and the like.

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 invention discloses a method for performing micro-hemorrhage focus recognition on brain images by using a deep learning algorithm, which comprises the following steps of: performing brain skull removal operation on magnetic sensitive weighted imaging images and phase images of a plurality of patients by using an HD-BET tool to obtain an intracerebral region map in a 2D image format; the method comprises the following steps of: firstly, firstly, carrying out brain detection on a brain region map, then, carrying out mesh division on each brain region map, dividing the brain region maps into mesh region maps with the same size, and then, training a YOLOv8 target detection model and a 3D-CNN network model by utilizing the mesh region maps, so that micro-hemorrhagic spots can be quickly predicted through the YOLOv8 target detection model, and then false positive micro-hemorrhagic spots are eliminated through the 3D-CNN network model. And a final micro-bleeding point detection result is obtained.

The utility model provides a protection device for an underwater animal experiment, and belongs to the field of underwater animal experiments. The brain cover part, the viscera cover part and the hip cover part are driven to be opened or closed through the opening and closing module, and the problem that in the prior art, an experimental device capable of protecting the brain and viscera of an experimental animal body from being damaged by vibration signals is lacked is solved. The device comprises a cover body module, a mounting base and an opening and closing module, the cover body module is fixedly mounted on the mounting base, and the opening and closing module controls the cover body module to be opened and closed; the cover body module comprises a brain cover part, an internal organ cover part, a hip cover part, a front side cover, a rear side cover, a bottom cover, a first partition plate and a second partition plate; the brain cover part, the viscera cover part, the hip cover part, the front side cover, the rear side cover, the bottom cover, the first partition plate and the second partition plate are each provided with an interlayer.

The invention discloses a three-electrode brain nursing instrument and a control method thereof.The three-electrode brain nursing instrument comprises a body, a plurality of electrodes are arranged on one side of the body, the three-electrode brain nursing instrument is characterized in that the electrodes comprise a first electrode cover, a second electrode cover and a third electrode cover, a circuit board is arranged in the body, a control module is arranged on the circuit board, and the control module is connected with the first electrode cover. The control module is electrically connected with the electrodes, and the control module achieves the functions of direct current forward stimulation, direct current reverse stimulation and alternating current stimulation by arranging cathodes or anodes in the first electrode cover, the second electrode cover and the third electrode cover. The control module can control the current magnitude, the stimulation time and the stimulation frequency of the electrodes according to different stimulation modes, and the functions of direct-current forward stimulation, direct-current reverse stimulation and alternating-current stimulation can be achieved through polarity conversion of the second electrode cover, the first electrode cover and the third electrode cover. Excitation or inhibition stimulation can be performed on the left dorsal outer side forehead lobe, the right dorsal outer side forehead lobe and the forehead center of the brain.

The invention discloses a cerebral hemorrhage MIT area detection method, device and equipment, a medium and a product, and relates to the field of brain image processing. The method comprises the following steps: dividing a brain imaging area into a plurality of areas with set angles, adopting different coil arrays in different areas, acquiring magnetic field and conductivity information in different coil arrays, and establishing a plurality of sample sets by taking the magnetic field information and the conductivity information as sample pairs; adopting the plurality of sample sets to train a stacked auto-encoder to obtain a plurality of MIT prediction models; determining the occurrence position of a focus, and selecting an array which enables the focus to be located in a detection side-n-degree to n-degree region in different coil arrays based on the occurrence position of the focus; acquiring magnetic field information by adopting the selected array, and obtaining phase information; selecting an MIT prediction model corresponding to the selected array, inputting the phase information into the MIT prediction model, and predicting to obtain conductivity distribution; and completing image reconstruction based on the conductivity distribution. The image reconstruction effect can be improved.

The invention relates to a brain blood oxygen monitoring method and device, and relates to the technical field of biomedical engineering.The method comprises the steps that a light attenuation signal generated when near-infrared light penetrates through the brain and a motion signal of the brain are synchronously collected, the motion signal and the light attenuation signal are coupled, and an infrared light-acceleration coupling signal is formed; preprocessing the infrared light-acceleration coupling signal to obtain a pure coupling signal; filtering the pure coupling signal through a quality evaluation mechanism to obtain an effective coupling signal; the effective coupling signals are input into a dual-wavelength ratio model, and the percutaneous blood oxygen saturation is calculated by fusing an experience calibration equation. By improving the algorithm, the accuracy of near-infrared brain blood oxygen monitoring can be effectively improved, and the signal error caused by head shaking can be reduced in combination with the motion monitoring function of the three-axis acceleration sensor.

The present invention relates to a ventricular drainage device, belonging to the technical field of medical devices. The ventricular drainage device comprises a controller, a drainage tube and a drainage bag, and also comprises: a fixing frame; a pushing mechanism; a limiting mechanism; a knocking mechanism; and a rubbing mechanism. The present invention uses a pushing mechanism and a limiting mechanism in coordination. In the process of the drainage tube draining the liquid in the patient's brain, the pushing plate of the pushing mechanism intermittently squeezes the drainage tube under the limiting action of the limiting mechanism, and the pushing plate will squeeze the drainage tube only when the movement direction of the pushing plate is the same as the flow direction of the liquid drained in the drainage tube, so that the pushing plate squeezes and pushes the blockage adhered to the inner wall of the drainage tube along the drainage direction, reducing the probability of the drainage tube being blocked, preventing the drainage tube from being blocked, resulting in a decrease in the flow speed of the liquid inside the drainage tube, and increasing the intracranial pressure of the patient, thereby ensuring the smoothness of the drainage and providing good protection for the patient.

The invention discloses a human body gait recognition method and device based on multi-modal fusion, and belongs to the field of human body gait assessment, and the method comprises the steps: collecting electromyographic signals of muscles at different movement positions of lower limbs; collecting near-infrared brain function imaging data of the brain in a gait image and an idle state; collecting pressure information of the planta pedis in the movement process; the electromyographic signals, the near-infrared brain function imaging data and the plantar pressure information are input into a multi-modal fusion model, feature extraction is conducted on the electromyographic signals, the near-infrared brain function imaging data and the plantar pressure information, and different features are fused through a cross attention mechanism; the classification algorithm identifies the fused features and performs gait evaluation, and as the electromyographic signals, the pressure information of the planta pedis and the near-infrared brain function imaging data are synchronously generated with the human body movement, the human body gait identified through the data is low in hysteresis quality and high in accuracy.

The invention relates to the field of small cerebral vessels, in particular to a medical image-based early feature analysis method and system for small cerebral vessels. The method comprises the following steps: acquiring a brain magnetic resonance image data set, preliminarily analyzing brain small vessel local features based on the brain magnetic resonance image data set, and generating a basic brain small vessel feature set; obtaining a clinical parameter set of the patient, analyzing the recessive influence factors according to the clinical parameter set of the patient, and generating a brain small vessel optimized feature set based on the basic brain small vessel feature set and in combination with the recessive influence factors; and performing visual fusion rendering according to the brain small vessel optimized feature set, and generating and outputting a record analysis log. In the process of early feature analysis of small cerebral vessels, doctors are assisted in accurately identifying the enlargement of the gaps around the vessels and simulators thereof, the misjudgment risk is effectively reduced, and the accuracy and reliability of early diagnosis of small cerebral vessel diseases are improved.

The invention provides a deep brain stimulation method and system based on controllable current vectors of high-channel-number electrodes, and the method comprises the following steps: implanting two electrodes into a target region, and enabling a nucleus of the target region to be located between the two electrodes; by selecting different contacts on the stimulating electrodes, multiple channels of induced currents in different directions are generated in a plane constructed by the two electrodes; vector current is formed through vector superposition of the induction current of each channel, and the vector current acts on the target nuclei. The high-channel-number electrodes are adopted, programming control is carried out between the two electrodes, accurate control over current vectors is achieved, control over the magnitude and direction of target region nuclei stimulation current is accurately achieved through current vector superposition, therefore, a brain lesion area can be stimulated more accurately, meanwhile, the current vectors can be accurately controlled, and the stimulation effect of the brain lesion area is improved. And unnecessary current is prevented from diffusing to surrounding normal brain tissues, so that side effects caused by stimulation of a non-target area are reduced.

The invention discloses a cerebral hemorrhage volume measurement method and related equipment. The method comprises the following steps: acquiring brain medical image data to be measured; inputting the brain medical image data into a three-dimensional segmentation network for cerebral hemorrhage region segmentation to obtain a cerebral hemorrhage region image; identifying a cerebral hemorrhage stage of the cerebral hemorrhage area image according to the cerebral hemorrhage area image; constructing a standard hemorrhage template based on the cerebral hemorrhage stage and the lesion anatomical position in the cerebral hemorrhage area image; performing gradient response analysis based on the feature map of the last convolutional layer in the three-dimensional segmentation network to generate an interpretation map; performing spatial similarity analysis based on the standard bleeding template and the interpretation graph to obtain a consistency score between the standard bleeding template and the interpretation graph; according to the cerebral hemorrhage area image, calculating an initial cerebral hemorrhage volume through a preset dual-path estimation model; and adjusting the fusion weight of the dual-path model according to the consistency score, and determining the target cerebral hemorrhage volume according to the adjusted fusion weight and the initial cerebral hemorrhage volume.

The invention provides a brain disease drug curative effect analysis and prediction system and method, and relates to the technical field of drug analysis. Multi-source heterogeneous data such as genetic variation spectrum, neural image topological characteristics, metabolic trajectory vectors and microbiome dynamic distribution matrix are integrated, and cross-modal dynamic fusion is realized through decomposition. The system constructs a drug prediction coupling network based on dynamic causal inference, analyzes overlapped potential intervention nodes of multiple disease mechanisms, and generates a disease-drug-phenotype multi-dimensional mapping library. A short-term drug response toxicity threshold is predicted through a metabolic entropy change model, a long-term neurological function degeneration trajectory is evaluated in combination with epigenetic drift, and an anti-fact strategy gradient is utilized to dynamically optimize a drug administration scheme. According to the invention, accurate fusion and dynamic analysis of multi-modal data are realized, a balance mechanism of curative effect and risk is established, and accurate drug combination sequence and administration strategy support can be provided for individualized treatment of complex brain diseases.

The invention discloses a cerebral hemorrhage heterogeneity quantitative evaluation system based on cyclic cross-attention clustering, and relates to the field of cerebral hemorrhage evaluation, and the system comprises an image segmentation module which is used for carrying out the three-dimensional segmentation of a brain plain-scan CT image through an nnU-NetV2 model, and outputting the binary masks of a hematoma region and an edema region; the feature extraction module is used for extracting high-dimensional pixel features from the mask region and generating an initial feature embedding matrix; the cyclic cross-attention clustering module is used for dynamically updating a clustering center through a multi-scale cyclic EM cross-attention mechanism and executing clustering analysis on the feature embedding matrix; the heterogeneity scoring module is used for calculating information entropy and generating hematoma and edema heterogeneity scores; and the prognosis prediction module is used for predicting the hematoma expansion risk and the neural function outcome through a machine learning model. According to the scheme, objective quantification of cerebral hemorrhage heterogeneity can be realized, and the prediction accuracy of the hematoma expansion risk and the neural function outcome is improved.

The embodiment of the invention discloses a brain operation path determination method and device, equipment, a medium and a medical system. The brain operation path determination method comprises the steps that a brain structure model and at least one target spot of a target user are obtained; based on the brain structure model, determining an information transfer relationship between each nuclear group sub-model and each nerve fiber sub-model; determining a plurality of candidate operation schemes based on the plurality of preset path starting points, the target group sub-model to which the at least one target point belongs and the information transfer relationship; for each candidate operation plan, performing quality evaluation on the candidate operation plan based on a quantitative evaluation model under at least one evaluation dimension, and determining a path quality attribute corresponding to each candidate operation plan; according to the method, at least one target recommended operation scheme is determined based on each path quality attribute, a plurality of candidate paths conforming to nerve conduction characteristics are automatically generated, and an optimal scheme is screened through a quantitative evaluation model, so that the brain tissue injury risk and the operation complexity are effectively reduced while the operation precision is improved.