72 results about "Multi organ" patented technology

The invention discloses a multi-organ medical image segmentation method based on a multi-feature fusion Swindow-Unet architecture, and belongs to the field of medical image processing. The core of the method is that CT and MRI images are input into a pre-trained CMFSA-UNet model for segmentation, and the model comprises an encoder, an MAFR module, an MFDF module, a decoder and a jump connection layer. CNN-Swin Transform double branches are adopted by the encoder, local details and long-range semantics are extracted, and Attention Gate reinforcement is carried out; the MAFR module widens a receptive field through double branches, combines an attention mechanism with residual connection, reduces the calculated amount and gives consideration to local and global features; and the MFDF module fuses multi-scale dense connection and frequency domain processing, so that feature loss is reduced. The decoder extracts features through Swin Transform Block, resolutions are recovered through 4 times of up-sampling, and the segmentation precision is optimized in combination with depth supervision and a mixed loss function. According to the method, local and long-range feature modeling is efficiently cooperated, precision and efficiency are balanced, segmentation global consistency, boundary accuracy and training stability are improved, the method is suitable for multi-modal multi-organ segmentation, and reliable support is provided for clinical diagnosis and the like.

The invention provides an abdominal multi-organ medical image segmentation method, and belongs to the technical field of image processing, and the method specifically comprises the steps: obtaining a to-be-segmented medical image, and inputting the to-be-segmented medical image into a segmentation model; performing feature extraction on the medical image through an encoder to obtain feature maps of different levels; a next generation Transform module is applied to a bottleneck layer to process the deep feature map extracted by the encoder so as to fuse global semantic information and local detail features; transmitting the features of each encoder layer to the corresponding decoder layer through jump connections, embedding a grouping feature fusion module in each jump connection, and performing multi-scale fusion on the low-level features from the encoders and the high-level features from the decoders; and the decoder performs up-sampling and decoding processing on the fused feature maps of each layer step by step, reconstructs a segmentation result map with the same size as the input medical image, and outputs the segmentation result map. Through the scheme disclosed by the invention, the segmentation efficiency, accuracy and adaptability are improved.

The invention discloses a sepsis patient organ function damage early warning judgment method based on machine learning, and the method comprises the steps: obtaining the multi-modal clinical data of a sepsis patient, including vital sign time sequence data, inspection data, treatment intervention data and static patient basic information; after data standardization processing, organ function associated features are extracted through a multi-scale feature fusion strategy, and an organ-level feature set is constructed; the method comprises the following steps of: obtaining a multi-organ collaborative early warning model, inputting the multi-organ collaborative early warning model into a pre-trained multi-organ collaborative early warning model, respectively constructing an inter-organ compensatory relation map by double branches of the model, quantifying organ injury risk contribution degree, and outputting a multi-organ functional injury risk matrix by combining with attention mechanism weighted fusion; and generating an early warning result based on the risk matrix and the organ specificity early warning threshold, including the injury risk level of each organ and the dominant risk key feature identifier, monitoring data update in real time, and dynamically adjusting the interval to update the early warning result. According to the invention, the limitation of traditional single-source data and single-organ early warning is broken through, and the early warning accuracy and real-time performance are improved.

The invention discloses an artificial intelligence chest multi-organ three-dimensional reconstruction method, and belongs to the technical field of medical image processing. Comprising the following steps: acquiring a plurality of preprocessed target CT images, and performing AI organ recognition and labeling on each target CT image; performing chest multi-organ segmentation model training according to the marked CT image to obtain a segmentation model for performing organ segmentation on the marked CT image, and obtaining multi-layer cross section data of each chest organ for performing three-dimensional reconstruction of a single chest organ; acquiring relative position information of each chest organ, and combining with the three-dimensional reconstruction model of the single chest organ to complete three-dimensional reconstruction of multiple chest organs to obtain an initial three-dimensional model; and converting the initial three-dimensional model into discrete point cloud data, constructing a point cloud anomaly detection network model to perform anomaly recognition on the point cloud data, and analyzing an anomaly recognition result by using a pathology basis large model to obtain a pathology result for performing corresponding marking on the initial three-dimensional model to obtain a target three-dimensional reconstruction model.

The invention discloses a highly anatomical real multi-organ ultrasonic CT (Computed Tomography) image data set construction method. According to an original image of an organ, an artificial intelligence segmentation model is used for segmenting the organ into multiple types of tissues, proper medium parameter values are allocated, and a small-scale and anatomically real ultrasonic CT phantom data set is generated; performing fine adjustment on the basic generative artificial intelligence model by adopting an ultrasonic CT phantom data set to generate large-scale new data, filtering out unreasonable image results, and re-adjusting medium parameter values exceeding a reference range, so as to generate a large amount of diversified and physically real phantom data; simulating to obtain an output scattered wave field after the interaction of the sound wave and the phantom, completing the construction of a large-scale medium parameter-wave field data pair, and obtaining a multi-organ ultrasonic CT image data set; the ultrasonic CT data set constructed by the method has the characteristics of large scale, diversification and high anatomy authenticity, and is used for realizing a high-resolution ultrasonic CT image reconstruction task.

The invention discloses a small sample abdomen multi-organ image segmentation method based on a prototype network and cross attention. The method comprises the following steps: firstly, preprocessing an abdominal computed tomography (CT) image, and completing resampling and intensity normalization; dividing the preprocessed image into a support set and a query set, and constructing a small sample segmentation task; a support image and a query image are input into a deep learning network model, a cross attention module is introduced into a multi-layer structure of an encoder to explicitly model foreground, background and boundary regions, interaction and fusion among different level features are enhanced, and pixel-by-pixel matching and distinguishing of support prototype and query features are realized in combination with a double-branch contrast learning structure. According to the method, a cross attention mechanism is introduced into multiple layers of the encoder, the correlation and boundary expression ability between the features are effectively enhanced, the discrimination and robustness of the model under the small sample condition are further improved through a double-branch contrast learning structure, and therefore under the condition that labeling data is limited, the accuracy and robustness of the model are improved. And efficient and automatic segmentation of multiple organs of the abdomen is realized.

The invention discloses a three-dimensional ultrasonic CT image reconstruction method based on a neural network agent model. The method comprises the following steps: constructing an anatomical real organ model, simulating a wave field by virtue of a numerical solver, and generating a multi-organ training data set; constructing and training a strong scattering neural operator (S2NO) based on an iterative structure of convergence Born series, so as to solve a partial differential equation with a large computational domain and high oscillation; collecting time domain wave field observation data of a to-be-detected part of a patient, and converting the time domain wave field observation data into frequency domain wave field observation data in a set frequency range through Fourier transform; using the trained S2NO as an agent model, executing a full waveform inversion algorithm, and iteratively reconstructing an ultrasonic CT image; according to the method, a three-dimensional ultrasonic CT image of an in-vivo clinical sample is reconstructed, the speed reaches about 10 times that of a traditional ultrasonic CT full-waveform inversion reconstruction algorithm, and the imaging quality similar to the standard magnetic resonance imaging (MRI) resolution is achieved.

The invention relates to a multi-modal ultrasonic data processing and report generation method and system based on retrieval enhancement, computer equipment, a storage medium and a computer program product, and the method comprises the steps that an ultrasonic image group is acquired, and the ultrasonic image group comprises at least two ultrasonic images of different organs; analyzing the ultrasonic images in the ultrasonic image group, and classifying the ultrasonic images according to organ types; obtaining a report set of each classified ultrasonic image; and finally generating a structured ultrasonic report according to the report set of each ultrasonic image. The problems that in the prior art, a large amount of irrelevant information is forcibly injected into the generation process, and serious semantic pollution is caused are solved; the technical effects that a complex multi-organ retrieval task is decomposed into a plurality of single-organ sub-tasks through organ-level retrieval space decoupling and dynamic routing mechanisms, so that the accuracy of report generation and the clinical reliability are improved are achieved.

The invention discloses a multi-organ communicating liquid core assembly. The multi-organ communicating liquid core assembly comprises an upper cover module, a transwell module and a porous cell culture base, at least one communicated perfusion channel is arranged at the bottom in the porous cell culture base, and the communicated perfusion channel is at least communicated with one culture hole; the transwell module comprises a plurality of small transwell chambers, the small transwell chambers are matched with the culture holes, the small transwell chambers are placed on the culture holes, and the upper cover module is used for sealing and covering the porous cell culture base. According to the liquid core assembly, a traditional culture plate and a micro-fluidic technology are combined, organoid high-throughput culture and multi-organ series co-culture are achieved, a series multi-organ culture mode is adopted, a dynamic culture liquid core assembly model is established in a simple and convenient mode, and the culture efficiency is improved. The bionic degree and the uniformity of the multi-organ series co-culture tissue are improved.

The invention relates to the technical field of forensic appraisal, and provides a method and system for constructing a drowning appraisal model based on virtual anatomy, and the method comprises the steps: obtaining image data corresponding to a plurality of corpses obtained through scanning the plurality of corpses based on different CT scanning devices; segmenting each piece of image data by using image processing equipment to obtain a VOI mask file corresponding to each corpse output by the image processing equipment; extracting original image omics characteristics in the VOI mask file; screening specific radiomics characteristics meeting screening conditions in the original radiomics characteristics; and constructing a multi-organ joint identification model based on a training set and a test set generated by the specific radiomics characteristics. According to the scheme, the phenomenon that the identification result is inaccurate due to the influence of external subjective and objective factors can be avoided, and the accuracy of drowning identification is improved.

The invention discloses a multi-organ medical image segmentation method and device based on weak supervision, relates to the technical field of image processing, and mainly aims to solve the problem that existing multi-organ medical image segmentation is poor in accuracy and effectiveness. Comprising the following steps: acquiring a collected multi-organ medical image, wherein the multi-organ medical image is obtained by computed tomography; performing standardized preprocessing on the multi-organ medical image; performing image segmentation on the multi-organ medical image subjected to standardization preprocessing based on a multi-organ segmentation network model subjected to model training to obtain a multi-organ image segmentation result; wherein the multi-organ segmentation network model is constructed by a double-branch network structure, the double-branch network structure comprises a shared encoder and two parallel decoding branches, and an overall loss function of the multi-organ segmentation network model comprises a cross entropy loss function configured with a dynamic threshold.

The invention discloses a multi-organ system biological age assessment method, and relates to the technical field of health assessment, and the method comprises the steps: S1, obtaining data; step S2, preprocessing the data; s3, screening biological age construction indexes of the multi-organ system; and S4, estimating the biological age of the multi-organ system. According to the method, the defects of an existing biological age model in the aspects of crowd applicability and prediction accuracy are overcome, accurate evaluation of the biological age of a multi-organ system is achieved, early recognition and accurate prevention and control of senescence-related diseases can be achieved, systematicness and heterogeneity of the senescence process are revealed, and the method is suitable for popularization and application. And more evidences are provided for pertinently intervening the senescence process.

The application relates to the technical field of medical data processing, and discloses a critical patient multi-organ function failure evolution path prediction system, which comprises a collection and preprocessing module, an adaptive dynamic characteristic extraction module, a physical dissipation constraint causal topology analysis module, a closed-loop feedback controller and a cascade failure path deduction module. The system extracts dynamic characteristics and calculates a physical effectiveness coefficient based on multi-modal physiological signals, uses the coefficient to correct transfer entropy to construct a multi-organ coupling network; the closed-loop feedback controller dynamically adjusts the embedding dimension parameter of the front-end characteristic extraction according to the total in-degree coupling strength of the network, forming a bidirectional constraint closed loop between the physical layer and the information layer. The application can effectively identify a pathological driving source, deduce the cascade propagation sequence of organ function failure, eliminate false causal connections through physical mechanism constraint and feedback regulation, and improve the accuracy of evolution path prediction.

The invention discloses a CT image tumor and risk organ joint segmentation method and system based on risk optimization, and mainly solves the problem of low segmentation precision caused by task optimization conflicts in the prior art. According to the implementation scheme, the method comprises the following steps: extracting sharing features of a CT image by using a sharing encoder; generating task-specific tumor risk prompt features and organ risk prompt features from the shared features through a mutual risk prompt learning module; and after the shared features and the risk prompt features are fused, the fused features are sent to a multi-gating hybrid expert MMoE decoding mechanism, the mechanism dynamically allocates weights for a group of expert decoders through a gating network, adaptive weighted decoding is carried out, and finally a segmentation result is generated. According to the method, through explicit modeling of task risks and adoption of a dynamic expert selection strategy, optimization conflicts are effectively solved, the accuracy and robustness of tumor and risk organ joint segmentation are remarkably improved, the method can be used for distinguishing tumor and related organs of a patient CT image, and powerful support is provided for clinical precise treatment.

The embodiment of the application discloses a kind of vein bionics microfluidic chip and its preparation and application method of simulating multi-organ interconnection.The chip includes a chip main body, and its microfluid channel system integrates at least three organ simulation units: a lung simulation chamber for culturing lung cells to simulate upstream organs that can produce systemic effects;An upstream colon cancer cell culture chamber for three-dimensional culture of colon cancer cells to simulate tumor primary lesions;And a downstream liver cell culture chamber for three-dimensional co-culture of liver cells and vascular endothelial cells to simulate the target organ of liver, and the three simulation chambers are communicated with each other by a vein bionics microfluid network, which simulates the complex blood circulation system of human body (including pulmonary circulation and systemic circulation), can realize the signal molecule transmission between organs, and simulate the invasion and metastasis process of colon cancer cells to the target organ of liver via the circulatory system after being affected by upstream lung signals.

The application discloses a three-dimensional ultrasonic CT image reconstruction method based on a neural network agent model. The application constructs an anatomically real organ model, simulates a wave field by means of a numerical solver, and generates a multi-organ training data set; constructs and trains a strong scattering neural operator (S 2 NO) based on an iterative structure of a convergent Borel series, which is used to solve a partial differential equation with a large calculation domain and high oscillation; collects time-domain wave field observation data of a part to be measured of a patient, and converts the time-domain wave field observation data into frequency-domain wave field observation data in a set frequency range through Fourier transform; uses the trained S 2 NO as an agent model to perform a full waveform inversion algorithm and iteratively reconstructs ultrasonic CT images; the application reconstructs three-dimensional ultrasonic CT images of in-vivo clinical samples, and the speed is about 10 times that of a traditional ultrasonic CT full waveform inversion reconstruction algorithm, and an imaging quality similar to that of standard magnetic resonance imaging (MRI) is achieved.

The application belongs to the technical field of intelligent medical treatment assistance, and provides a multi-organ motion coupling compensation system based on a space-time prediction model and an execution method thereof, which comprises a multi-source heterogeneous data acquisition and preprocessing module, a double-dimension gold standard construction module, a space-time fusion prediction model construction module, a training and verification module, a time sequence trajectory prediction module and a motion compensation instruction generation module. Through time sequence synchronization and space registration of multi-modal data, the application uses a space-time Transformer architecture to construct a prediction model, accurately quantifies multi-organ motion coupling coefficients, and predicts an output motion trajectory that is physically reasonable and meets accuracy standards. Meanwhile, relying on double gold standards and an optimized training strategy, combined with a parameter efficient fine-tuning strategy, the application realizes personalized adaptation for specific patients, solves defects such as low multi-source data synchronization accuracy and insufficient coupling relationship modeling in existing radiotherapy motion compensation technologies, and promotes the clinical landing and upgrading of precise radiotherapy technologies.

The invention belongs to the technical field of biotechnology and medicine, and particularly relates to application of an Anapc11 gene / protein to screening of drugs for treating hyperuricemia-related multi-organ dysfunction. According to the invention, the Anapc11 gene is used as a molecular target or a drug action target, and is used for screening or preparing drugs for treating multi-organ dysfunction (including liver injury, testis dysfunction, intestinal barrier destruction and the like) caused by hyperuricemia. The invention provides a new application of the cell cycle promoting complex subunit E3 ubiquitin ligase Anapc11 gene, provides a new molecular target for treating hyperuricemia related multi-organ dysfunction, and breaks through the limitation that the existing uric acid reducing medicine is only limited to a metabolic link; a new direction and a good application prospect are provided for developing innovative medicines with organ protection effects.

The application discloses a kind of multi-organ intelligent segmentation method and device based on Eff-Unet-SE network, belong to medical image processing technical field.The method includes: obtaining MRI image dataset and label text data, and it is preprocessed;The image dataset after the pre-processing is carried out data division and data enhancement;Eff-Unet-SE network is constructed, and it is trained, verified and tested using the image dataset and label text data, to obtain optimal network;The MRI image data to be segmented is input to the optimal network, to obtain segmentation result.The application is analyzed to medical MRI image, on the basis of U type network model, in combination with the Efficient and SE network module of deep learning, can accurately separate out large intestine, small intestine and stomach, improve the reliability of segmentation result, also improve segmentation efficiency, greatly reduce the work burden of doctor.

This invention relates to a multi-organ biological age and disease risk assessment system based on chest CT radiomics, belonging to the field of medical image computer-aided analysis technology. The invention aims to address the problem that a single global image age cannot characterize organ-specific aging and lacks a closed-loop clinical risk quantification mechanism. The technical solution includes: receiving chest CT radiomics features and demographic parameters of the subject through an input layer; performing spatial mapping using a feature layer; outputting a 10-dimensional biological age through a three-stage cascaded machine learning model in the age prediction layer; calculating the age acceleration rate through a bias correction layer; quantifying the disease risk multiple by coupling a Cox proportional hazards model through a risk assessment layer; and finally, generating clinical interpretation through a large language model driven by an intelligent reporting layer. This invention achieves low-cost and non-invasive organ-level aging assessment, accurately capturing allometric aging, and improving the efficiency of clinical risk stratification and the standardization of interpretation.

The invention discloses an intelligent ICU monitoring management system, and relates to the technical field of ICU supervision. The intelligent I CU monitoring management system comprises a deployment and data acquisition module; a compensatory marker in blood is obtained, and intracranial pressure micro-vibration generated by cerebral blood flow pulsation is captured; a bionic compensation module; obtaining the compensation characteristic value of each organ; an environment linkage module; the logic configuration module is used for realizing real-time dynamic coupling of an I CU physical environment and a physiological state of a patient and establishing trigger logic of environment linkage; a nerve regulation module; and performing nerve regulation and control on the patient by combining the trigger logic of the blood compensation marker signal and the vibration signal. Blood markers and intracranial pressure micro-vibration signals are synchronously collected through the microenvironment probe and the piezoelectric film array, a multi-organ compensation model is constructed in combination with the bionic compensation module, the limitation of traditional single-parameter monitoring is broken through, compensation characteristics of a single organ can be recognized, the whole-body compensation state can be reflected through multi-organ coupling analysis, and the compensation accuracy of the whole body is improved. And the potential risk can be recognized earlier.

Disclosed are means for predicting (i) multi-organ metastasis and / or multifocal metastatic disease, and (ii) overall survival (OS) and progression-free survival (PFS) of subjects suffering from cancer, the predictions being based on the number and size of circulating cancer-associated macrophage-like cells (CAML) found in a biological sample, such as the blood, of the subject.

The application provides a kind of based on multi-modal imaging integration analysis living body detection method and system, it is related to multi-modal fusion technical field, the application obtains the multi-modal imaging data of multiple organs of living object;According to multi-modal imaging data, respectively calculate fluorescence intensity distribution characteristics, tissue density value characteristics and proton density signal characteristics;Fluorescence intensity distribution characteristics and proton density signal characteristics are respectively associated with tissue density value characteristics and are processed cross-modality, obtain first association relationship, and second association relationship;First association relationship and second association relationship are fused with parameters, obtain cross-modality feature matching data set, and it is parameter calibrated, obtain calibrated cross-modality feature matching data set;The cross-modality characteristics of each organ are analyzed, and the living body detection result is obtained;Realize the collaborative processing of the physiological and structural characteristics of multiple organs of living object, improve the accuracy and reliability of cross-modality biological feature recognition.

This invention belongs to the field of medical image data processing technology, specifically disclosing a multi-agent medical image report generation method and system based on fine-grained organ perception. The method includes the following steps: constructing an organ-level medical image dataset and an organ-level report dataset; inputting the organ-level medical image dataset and organ-level report dataset into a multi-agent system based on fine-grained organ perception to extract features, performing multimodal fusion and decoding to obtain an organ-level predicted report; serializing the organ-level predicted report and then sending it to the agent semantic interaction module for unified semantic modeling to generate the final medical image report. This technical solution utilizes a fine-grained organ perception multi-agent architecture to achieve organ-level fine-grained cross-modal modeling and organ-level report prediction, and improves the consistency and completeness of multi-organ information through the agent semantic interaction module, thereby enhancing the accuracy and reliability of the generated medical image report.

The invention belongs to the field of biological medicine, and particularly relates to a construction method and application of a spontaneous obesity model of a systemic GPX3 knock-gene mouse. The construction method comprises the following steps: preparing a precisely targeted GPX3 gene, knocking out a gene editing tool mixture of a specific segment (SEQ ID NO.1) of the GPX3 gene, transferring the gene editing tool mixture into a fertilized egg, and mating a mouse until a stably inherited GPX3 gene knockout homozygous mouse is obtained. The GPX3 is knocked out through the CRISPR / Cas9 technology, the spontaneous obese mouse model which cannot be achieved in the prior art is successfully obtained, the model can simulate obesity-related multi-organ lesions and diabetes mellitus progress, and a reliable tool is provided for obesity mechanism research and drug research and development.

Applicants engineered peptide-modified AAV9 capsids for system-wide enhanced organ transduction by directly engineering a binding interaction with the human GPI-linked glycoprotein CD59. Adeno-associated vimses (AAVs) are used by numerous approved and investigational gene therapies. However, native AAVs have limited tissue tropisms and have comparatively low extrahepatic delivery efficiencies. While capsid engineering has largely focused on targeting specific organs, treating multisystem disorders requires efficient gene delivery to many organs. Here Applicants describe novel engineered capsids that bind CD59.

The invention discloses a method for predicting harm of a new pollutant PFAS to human health based on a large language model, and aims to solve the problems of single structural feature expression, lack of toxicological semantic fusion, insufficient multi-organ toxicity prediction capability and the like in the prior art. According to the method, PFAS multi-dimensional structure features are extracted through combination of a graph neural network and molecular fingerprints, toxicology literature semantic evidence is mined based on a large language model, structures and semantic evidence vectors are fused by adopting a Transform multi-task framework, a multi-label classification model covering six organ systems including the liver, the kidney and the nerves is constructed, the toxicity probability of each organ is output, and the toxicity probability of each organ is calculated. Five risk levels are divided, and interpretable results are provided in combination with structure fragments and literature evidence. According to the method, the accuracy and stability of toxicity prediction are improved, and PFAS substitute screening and health risk management and control are assisted.

The invention relates to a detachable multi-organ chip co-culture device, and relates to the technical field of biological tissue engineering. Comprising an upper-layer fluid channel layer chip, a lower-layer micro-pit cell culture layer chip and an osmotic pump, the upper-layer fluid channel layer chip and the lower-layer micro-pit cell culture layer chip are respectively divided into five independent functional units, and the structures of the independent functional units are mutually independent; each independent functional unit of the upper fluid channel layer chip comprises a fluid channel structure; each independent functional unit of the lower-layer micro-pit cell culture layer chip comprises a micro-pit array structure and a micro-cavity, and the micro-pit array structure is arranged at the bottom of the micro-cavity; the upper-layer fluid channel layer chip and the lower-layer micro-pit cell culture layer chip are detachably combined and connected through a tenon-and-mortise structure; the osmotic pump is connected to the outlet end of the culture device and is used for constructing a dynamic perfusion culture environment. According to the invention, high-throughput culture of multiple organs can be realized, and the cell viability and proliferation state are good under a dynamic perfusion condition.