68 results about "Clinical pathology" patented technology

The invention provides a circulating tumor cell recognition model training method and system based on deep learning, and the method comprises the steps: obtaining a multi-channel pathological sample image set of circulating tumor cells, carrying out the cross-modal feature extraction of a multi-channel pathological sample image, generating a fusion feature vector containing the morphological features and microenvironment features of the cells, and carrying out the recognition of the circulating tumor cells through the fusion feature vector. A deep convolutional network with a multi-stage training strategy is constructed, network parameters of the deep convolutional network in the training process are verified and adjusted by adopting a hierarchical cross validation mechanism, and finally a final recognition model is generated based on the verified and adjusted network parameters. The final recognition model is configured to receive the clinical pathology image stream and output localization coordinates and classification confidence of circulating tumor cells. According to the method, a circulating tumor cell recognition result can conform to a cell space distribution rule and has clinical pathological diagnosis interpretability, and the problems of high false positive rate and insufficient cell subtype discrimination caused by neglecting of microenvironment interaction in a traditional method are solved.

The invention relates to the technical field of medical image analysis, and discloses an HER2 positive breast cancer auxiliary prediction method based on multi-modal data fusion. According to the method, preoperative MRI, clinical pathology data and RNA-seq data meeting quality control standards are obtained through dynamic case screening, traditional image features, multi-scale Hessian matrix blood vessel-structure features and topological persistent coherent curvature features are extracted through multi-plane segmentation, and key features are obtained through three-level screening. A multi-factor logistic model fusing radiomics, clinical features and immune scoring is further constructed, a pCR probability value and a feature contribution heat map are output, and MRI data quality control, real-time analysis and interactive report generation are achieved through a matched computer system. The method breaks through the limitation of single modal analysis, dynamically associates the image features with the molecular mechanism, and provides interpretable decision support for precision medical treatment.

The invention belongs to the technical field of medicine, and discloses a method and system for predicting anastomotic fistula occurring after colorectal cancer operation, and the method comprises the steps: obtaining clinical pathology data and laboratory analysis data of a patient; filling missing data in the clinical pathology data and the laboratory analysis data; screening key indexes by using a single-factor regression analysis technology; performing multi-factor regression analysis on the key indexes, and screening to obtain independent prediction factors for predicting whether the colorectal cancer patient has the anastomotic fistula or not after operation; based on the independent prediction factors obtained through screening, an anastomotic fistula prediction model is constructed and trained, and the trained anastomotic fistula prediction model is used for predicting the postoperative anastomotic fistula occurrence probability of the colorectal cancer patient. The method can more accurately predict the probability of occurrence of anastomotic fistula of a colorectal cancer patient after an operation, has better accuracy and reliability, can facilitate preoperative prediction of the probability of occurrence of anastomotic fistula for clinical doctors, and assists in guiding treatment decision.

The invention is applicable to the technical field of pathological analysis, and provides a biological sample pathological detection method and system based on a characteristic signal threshold, and the detection system comprises: a molecular probe labeling module for performing molecular probe labeling on a standard sample and a biological sample to be detected; the three-dimensional gel electrophoresis device is used for carrying out three-dimensional gel electrophoresis separation on the marked sample; the three-dimensional gel electrophoresis signal collection module is used for extracting and collecting sample signals after electrophoretic separation; the tissue characteristic signal threshold establishing module is used for establishing a characteristic signal threshold of the standard sample; and the automatic diagnosis module is used for detecting the biological sample to be detected by utilizing the established characteristic signal threshold. The biological sample pathology detection method based on the characteristic signal threshold value shows excellent performance in clinical pathology detection, the clinical pathology work burden can be relieved, the clinical pathology work accuracy can be improved, and the biological sample pathology detection method has wide application prospects and huge market potential.

The invention relates to a pathological section intelligent auxiliary differential diagnosis system based on multi-modal fusion, in particular to the field of clinical pathology, semantic unification of multi-modal data is achieved through meta-task construction and a cross-modal alignment technology, and transferable diagnostic knowledge is extracted by utilizing a meta-learning framework; the method combines a generative model and knowledge constraints to generate high-quality synthetic data, and finally fuses real and synthetic samples through a self-adaptive diagnosis mechanism, thereby remarkably improving the differential diagnosis capability of rare lesions, effectively solving the problem of model generalization in a training data scarcity scene, and improving the accuracy of model identification. And efficient and reliable intelligent auxiliary decision support is provided for clinical pathological diagnosis.

The invention discloses an endometrial cancer nursing treatment response prediction method based on multi-dimensional pathology assessment, and relates to the technical field of intelligent diagnosis prediction, and the method comprises the steps: collecting and standardly managing patient information data, and constructing a dynamically updated and quality-controllable multi-modal clinical pathology database. Performing multi-scale processing on the image and immune features, extracting tumor structures and immune cell distribution, and constructing a structured immune feature database; and multi-modal data are fused, an intelligent evaluation model is established, nursing treatment response is predicted, and dynamic optimization of the model is realized through clinical feedback. According to the invention, a standardized and multi-modal clinical pathology database is constructed, image, immune and molecular features are fused, multi-scale spatial information is accurately extracted, and a high-precision intelligent prediction model is established. And by introducing a dynamic feedback mechanism, the model can be adaptively optimized, and the accuracy and clinical practicability of nursing treatment response prediction are remarkably improved.

The invention provides a PD-L1 immunohistochemical section tumor identification detection method and system, and the method comprises the steps: collecting various organ PD-L1 sections of a clinical pathological experiment, carrying out high-resolution scanning, pre-identification screening and cloud data storage, and obtaining a PD-L1 section data set of various organs; automatically labeling each tumor cell labeling pixel-level mask, automatically generating a labeling bounding box, and obtaining a tumor cell region labeling data set; according to the tumor cell region labeling data set, preprocessing all positions labeled as effective regions, creating a YOLO v8 deep learning model, training to gradually adjust network weight, and outputting a prediction result of the YOLO v8 deep learning model; and analyzing and calculating whether the prediction evaluation index information of the YOLO v8 deep learning model accords with a training target evaluation index, completing model training, and carrying out accurate positioning and pixel-level region identification detection on PD-L1 immunohistochemical pathological section tumor cells.

The invention discloses an immunotherapy prediction model training method, calling method, device and equipment, and the method comprises the steps: obtaining a clinical pathology image and clinical pathology information, the clinical pathology image is a pathology image of an immunotherapy patient before treatment, and the clinical pathology information is a pathology image of the immunotherapy patient before treatment; the clinical pathological information comprises pre-treatment clinical information and post-treatment prognosis information of the immunotherapy patient; marking the clinical pathology image to obtain a marked image, and performing feature extraction on the marked image based on the clinical pathology information to obtain pathology omics features; and carrying out model training by adopting the pathological omics characteristics to obtain an immunotherapy prediction model. The image features are accurately extracted through the pathological information, then the image features are used for model training, the precision of the model can be improved, and the error of the model is reduced.

The invention discloses a colorectal cancer postoperative venous thromboembolism risk prediction method and system, and the method comprises the steps: obtaining clinical pathology data of a historical patient before the patient receives a total proctectomy, carrying out the preprocessing of the clinical pathology data, employing a standardized score to detect an abnormal value in the clinical pathology data, carrying out the filling of a missing value, and carrying out the prediction of the risk of the venous thromboembolism. Performing standardization processing on the continuous variables; based on the preprocessed clinical pathology data, extracting clinical pathology features by adopting Lasso regression and grid search; constructing a Stacking integration model based on a random forest, a CatBoost model and logistic regression, and training the Stacking integration model by using the extracted clinical pathological features; and by using the trained Stacking integrated model, processing clinical pathology data of a target patient before the target patient receives the total proctectomy to obtain a prediction result. The colorectal cancer postoperative venous thromboembolism risk can be effectively predicted, and support is provided for clinical decision making.

The invention provides a lung cancer brain metastasis identification method and system based on multi-modal data, and the method comprises the steps: obtaining clinical pathology data, lung CT image data and brain multi-parameter MRI image data of a user, and carrying out the data preprocessing; performing anomaly detection on the preprocessed data to obtain a corresponding anomaly score; weighting various abnormal scores, and performing normalization processing to obtain a comprehensive abnormal score; determining a prediction threshold value of the brain metastasis risk of the lung cancer according to the maximum Youden index; comparing the comprehensive abnormal score with a prediction threshold, and determining a prediction result; when the prediction result shows that the user is a lung cancer brain metastasis patient, dividing the preprocessed brain multi-parameter MRI image data into a plurality of areas with preset sizes, and respectively calculating reconstruction errors; and mapping the reconstruction error of each region to generate an error mapping graph so as to reflect the high-risk region of the lung cancer brain metastasis.

The invention belongs to the technical field of focus positioning, and particularly relates to a focus positioning method and system based on an infrared thermal imaging system. According to the method, temperature distribution, edge features, temperature gradient and time domain change information are integrated, and temperature and physiological compensation are combined, so that abnormal temperature information is more real and reliable, the accuracy of focus positioning is improved, the misdiagnosis rate is reduced, individualized data such as the body type of a patient, the blood perfusion rate and the environment temperature are introduced, and the accuracy of focus positioning is improved. The detection result can better adapt to individual differences, the pertinence and reliability of diagnosis are improved, abnormal features can be more accurately extracted by comparing an actual infrared image with a theoretical temperature distribution diagram, meanwhile, the credibility and clinical application value of focus positioning are improved in combination with a clinical pathology database, and the diagnosis accuracy is improved. The generated lesion probability thermodynamic diagram visually displays the core area and the diffusion boundary of the lesion, provides a visual reference basis, and is helpful for finding lesions earlier and formulating a reasonable treatment scheme.

The invention discloses an anti-soluble transferrin receptor monoclonal antibody as well as a preparation method and application thereof, and belongs to the technical field of antibody detection. According to the antibody, an L101-F760 amino acid sequence of human TfR protein is used as an immunogen, specific screening of B cells, reverse RNA (Ribonucleic Acid) into cDNA (Complementary Deoxyribose Nucleic Acid) through RT-PCR (Reverse Transcription-Polymerase Chain Reaction), then homologous recombination is performed to construct heavy and light chain vectors, and finally multiple rounds of screening are performed to obtain the purified sTfR monoclonal antibody, so that the sTfR protein can be specifically recognized, and the antibody has the advantages of high specificity and high sensitivity and can be widely applied to the field of immunotherapy. The natural sTfR protein in human serum can be recognized. The kit is applied to the detection and screening fields of immunohistochemistry, immunofluorescence chemistry, western blot, co-immunoprecipitation, indirect ELISA, flow cytometry and the like, especially has low background and no interference of non-specific binding impure protein during immunohistochemistry detection, can obtain more accurate detection and evaluation results, avoids interference of false positive and false negative, and has high detection accuracy. The method has great application value in the aspects of basic scientific research and clinical pathology detection.

The invention provides a self-supervised cancer detection system based on subspace clustering. The system comprises a model training module, an image processing module and a result output module. According to the invention, a classification model for cancer discrimination is established and is applied to intelligent diagnosis of clinical pathology; when a to-be-detected pathological image is received, preprocessing operation is firstly carried out, and the preprocessing operation aims at extracting key features in the pathological image so as to better carry out cancer discrimination; inputting the preprocessed pathological image into a trained cancer discrimination classification model for discrimination to obtain a prediction result of the to-be-detected pathological image; the prediction result is displayed on a diagnosis and treatment interface of a pathologist, meanwhile, a diagnosis report is generated, and the pathologist makes more accurate cancer diagnosis and treatment decisions according to the prediction result and the diagnosis report, so that the accuracy and efficiency of pathological diagnosis are improved; therefore, a more accurate cancer judgment result is provided, and doctors are assisted in diagnosis and treatment decision making.

The invention discloses a medical synthetic data analysis method and device based on causal reasoning and a medium. The method comprises the following steps: generating medical synthetic data through a generator created according to a real clinical data set; analyzing the real clinical data set and the medical synthetic data to obtain a first causal diagram and a second causal diagram, and comparing the two diagrams to identify an abnormal causal edge corresponding to the medical synthetic data; determining a path corresponding to the abnormal causal edge in the first causal graph, extracting risk factors and corresponding results in the path, and respectively calculating an average processing effect and an effect difference value of the risk factors on the results in the real clinical data set and the medical synthetic data; and determining an analysis result of the medical synthetic data according to the effect difference value. According to the method, the medical synthetic data quality can be deeply analyzed, the fidelity of the synthetic data to a real-world clinical pathology causal mechanism is verified, the specific link of causal distortion is accurately positioned, and clear and executable guidance is provided for the correction data generation process.

The invention relates to the field of image processing, in particular to a fundus disease image segmentation method and system based on multi-mode focus simulation. According to the method, for a simulation focus image of an OCT mode, image denoising, OCT layer information detection, layering abnormity judgment, RPE layer inclination angle determination, to-be-implanted OCT focus-free eye fundus image and focus implantation position determination, simulation focus size adjustment and simulation focus pixel value reconstruction are carried out in sequence, and an OCT multi-class synthetic eye fundus disease image and a segmentation label thereof are generated; and for the simulation focus image of the CFP mode, a CFP multi-class synthetic fundus disease image and a segmentation label thereof are generated by selecting a proper background image, adjusting the brightness of the background image, cutting off the edge of the simulation focus and reconstructing the pixel value of the simulation focus in sequence. Clinical pathology constraints are followed, fundus disease image data diversity is increased, meta-task enhancement is realized, and segmentation precision of a focus segmentation network is improved.

The invention relates to a liver cirrhosis prediction and noninvasive diagnosis system and method for autoimmune hepatitis, and belongs to the technical field of medicine. The system comprises a data acquisition module, a data processing module and a model construction module. The data acquisition module is used for acquiring blood detection indexes of a patient, including total protein, albumin, total bilirubin, direct bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), glutamyltranspeptidase (GGT), urea, creatinine, platelet count (PLT) and the like. The model building module is combined with blood detection data and clinical pathology data, a statistical analysis method such as binary Logistic regression is adopted, and a liver cirrhosis prediction model is trained and built. Based on four key blood detection indexes, the method can effectively predict the occurrence of liver cirrhosis of AIH patients, and especially provides a scientific reference for noninvasive diagnosis and curative effect monitoring for patients who cannot perform liver puncture or reject liver puncture.

The invention provides a clinical application level pathology large model construction method and system based on weak supervised learning. The method comprises the steps of performing weak supervised training on each historical clinical pathology image, adjusting an upstream learning parameter of a large model according to a training result, constructing an upstream task of the large model, and performing segmentation decoding on each weak supervised training result, determining a segmentation result, adjusting downstream learning parameters of the large model, constructing a large model downstream task, obtaining upstream fine features and downstream fine features, constructing an upstream and downstream cooperative task of the large model, and generating a pathological large model in combination with the large model upstream task and the large model downstream task. And inputting the current clinical pathological image into the large pathological model for pathological information identification, obtaining a plurality of pathological identification tags corresponding to the patient, and generating a pathological report of the patient, thereby reducing the calculation complexity when processing a high-resolution image, optimizing a collaboration mechanism among different tasks in the large model, and improving the efficiency of the large model. The method can be widely applied to pathological recognition technologies.

The invention relates to the technical field of animal model construction, and discloses a non-alcoholic steatohepatitis model and a construction method and application thereof. The construction method comprises the following steps: performing stable overexpression on an AKT gene in the liver of a mouse by adopting a high-pressure caudal vein transfection technology, and feeding the mouse by adopting a high-fat feed. The non-alcoholic steatohepatitis model constructed by the construction method provided by the invention has the characteristics of liver inflammatory cell infiltration, liver fat deposition, insulin resistance, dyslipidemia and the like, has high clinical pathology similarity with NASH, has a remarkable steatohepatitis phenotype, can be used for researching the occurrence and development mechanism of NASH and evaluating the treatment effect of drugs, and has a wide application prospect. The method is of great significance to prevention and treatment of generation and development of NASH; meanwhile, the construction method is simple to operate, does not need transgenic animals, is short in feeding period, low in cost and high in efficiency, can effectively shorten the establishment period of the non-alcoholic steatohepatitis animal model, and has a good application prospect.

A system and method for generating a healthcare report from a pathology test report are provided. In one form, the system includes at least one processing device configured to: extract pathology data from the pathology test report using a machine learning model; provide, on a data interface, and to a user, the pathology data for review by the user; receive, on the data interface, and from the user, corrected pathology data; provide at least part of the corrected pathology data to the machine learning model to further improve the machine learning model; retrieve auxiliary content according to the corrected pathology data; and generate the healthcare report from the corrected pathology data and the auxiliary content.

A method for determining a treatment for a cancer patient is disclosed, the method comprising measuring concentrations of multiple clinical pathology markers for gradient enhanced machine learning analysis.

The application relates to a whole-body organ metabolism network analysis method, device, equipment and storage medium. The method comprises the following steps: collecting a PET image of a subject, obtaining SUV images, Ki images and TAC data according to the PET image, and collecting physiological information, clinical pathology information and daily habit information of the subject; metabolism characteristics are extracted based on the PET images, SUV images and Ki images corresponding to the region of interest; the TAC data of each organ is curve-fitted by using the PET image, and the metabolism disturbance under different organs is represented by using the fitting residual error; a whole-body organ metabolism network is constructed according to the physiological information, clinical pathology information, daily habit information, metabolism characteristics and metabolism disturbance; and the whole-body organ metabolism of the subject is analyzed through the whole-body organ metabolism network. The application avoids individual differences, can more comprehensively capture the metabolism correlation among whole-body organs of a specific disease, and is helpful for early diagnosis, accurate evaluation and individualized treatment of clinical diseases.

The present invention relates to the application of toxicogenomics methods to the detection and / or prediction of histopathological abnormalities in human or animal subjects based on clinical pathology data. It has been observed that reliable results can be obtained even in the absence of image data. A computer-implemented method for predicting the presence of a histopathological abnormality in an organ of a human or animal subject based on clinical pathology data comprises receiving clinical pathology data obtained from the human or animal subject, applying an analytical model to the clinical pathology data, the analytical model being configured to output a result indicative of the likelihood of the presence of the histopathological abnormality in the organ of the human or animal subject, and outputting the result.

The application relates to the technical field of medical data processing, and discloses a knee joint three-dimensional movement analysis platform for clinical auxiliary diagnosis, which comprises the following steps: a data processing module adopts Magic software to collect knee joint three-dimensional movement original data, carries out coordinate correction and model processing, and generates a model group. A model analysis module imports the model group into a C3D clinical window, realizes real-time visualization of a three-dimensional movement track, calculates biomechanical parameters, and evaluates clinical pathology correlation. A man-machine interaction control module provides customized interaction logic and a graphical interface, supports parameter input, analysis process control, real-time monitoring, and automatic generation of a diagnosis report. A cloud publishing module converts the diagnosis report and the interface into a PHP dynamic webpage, deploys the webpage through HTTP, supports SSL encryption, multi-user concurrent access, and cross-platform remote calling. The application supports real-time visualization of a knee joint three-dimensional movement track, and improves data reliability.

The invention discloses a curative effect prediction method based on multi-organ metastasis genome data, and belongs to the technical field of medical models, and the method specifically comprises the following steps: collecting clinical pathological characteristics, multi-organ metastasis genome data and a treatment scheme of a breast cancer patient, and recording a metastasis part and a load state; dimensionality reduction is conducted on high-dimensional genome data through a regularization algorithm, feature importance is evaluated in combination with a nonlinear model, and clinical, treatment and genome features related to treatment response are screened out; inputting the screened features into a machine learning and deep learning framework, randomly dividing a training set and a test set in a layered manner, optimizing hyper-parameters through cross validation, and constructing a classic machine learning set model and a deep learning model based on an attention mechanism; disturbing test queue treatment scheme data, evaluating the consistency of model recommendation and an actual scheme, and verifying the prediction capability and clinical practicability of the model; according to the method, multi-dimensional data are integrated, and the curative effect prediction accuracy of the metastatic breast cancer is improved.

The invention belongs to the technical field of intelligent drug delivery control, and particularly discloses a colon tumor drug delivery method and system based on AI control, and the method comprises the steps: collecting and preprocessing whole genome sequencing, radiomics, clinical pathology and historical drug treatment response data of a patient, extracting tumor image features, and carrying out drug delivery. The method comprises the following steps: screening oxaliplatin response related gene mutation markers by combining Lasso regression with a Cox model, forming feature vectors by t-SNE dimensionality reduction clinical pathological data, training a model through a gradient boosting tree and deep neural network fusion algorithm, taking oxaliplatin response probability as output, and recommending an FOLFOX scheme or an FULV scheme according to a model result. According to the method, precision and individuation of chemotherapy are achieved, unnecessary toxic and side effects are reduced, the life quality and lifetime of a patient are improved, and the problems that in the prior art, dependence on experience, single data dimension, no dynamic adjustment mechanism and poor stability are solved. Therefore, the problems of low response rate of oxaliplatin and obvious side effect are solved.

The invention provides a method, a device, equipment and a medium for predicting the expression level of renal cancer HIF-2alpha. The method comprises the following steps: S10, acquiring clinical pathological data and an abdominal enhanced arterial phase image of a patient with renal clear cell carcinoma; s20, sketching a three-dimensional region of interest of the tumor through the abdominal enhanced arterial phase image; s30, extracting image radiomics characteristics from the tumor three-dimensional region of interest, screening the image radiomics characteristics through Z-score standardization processing, stability test, an mRMR algorithm and an LASSO regression algorithm in sequence to obtain screened characteristics, and calculating to obtain a radiomics score through the screened characteristics; s40, screening out independent clinical predictive factors related to HIF-2alpha expression from the clinical pathology data, wherein the independent clinical predictive factors comprise WHO / ISUP classification and clinical T classification; and inputting the independent clinical prediction factor and the radiomics score into a joint prediction model to obtain a prediction score, and evaluating the probability of HIF-2alpha expression by using the prediction score.

The invention discloses an endometrial cancer typing system based on a multi-dimensional molecular marker, and the system comprises a data input module which is used for receiving multi-dimensional molecular marker state information which is inputted by a user and is related to an endometrial cancer sample, and the information contains genomics, apparent and expressive data and clinical pathology data; the data processing module is configured to sequentially execute multi-level typing logic based on the state information, and the logic comprises the following steps: typing based on POLE gene mutation state preferentially, then typing based on mismatch repair function and p53 function state in sequence aiming at an unshaped sample, and finally carrying out joint layering based on prognosis association indexes; and the typing result output module is used for outputting the final molecular subtype. Through multi-dimensional data cross validation and dynamic layering, the problems that an existing typing technology is single in dimension and poor in clinical applicability are solved, accurate and efficient typing of endometrial cancer is achieved, and reliable technical support is provided for individualized diagnosis and treatment.

The present invention relates to a method for determining a treatment for a cancer patient. The method includes measuring a variety of clinical pathological marker concentrations for gradient enhanced machine learning analysis.

The invention provides a system for predicting the applicability of triple negative breast cancer neoadjuvant immunotherapy. Specifically, the system comprises at least one detection unit comprising determining a multi-modal feature of a subject to be detected, the multi-modal feature comprising, or consisting of, a clinical pathological feature, a copy number variation feature, a plasma protein feature, and a somatic mutation feature; the system further comprises at least one data analysis unit and at least one judgment unit. The system is used for predicting new auxiliary immunotherapy of the triple negative breast cancer, and has excellent sensitivity and specificity.

The invention belongs to the field of surgical instruments, and provides a surgical minimally invasive surgery endoscope artificial intelligence system which comprises a control box, a mechanical arm, a visual sensor, an inner needle assembly and an air bag. The air bag is used for being placed in an operation opening, a minimally invasive hole or a natural hole of a human body in a closed state and expanding by transmitting air pressure to support and open organs or tissues; the visual sensor comprises a CMOS sensor, a light path assembly, a light filter, a light field and a prism assembly, the visual sensor is combined with the endoscope or the endoscope, and the visual sensor is driven by the mechanical arm to enter a human body along a surgical opening, a minimally invasive hole or a natural hole of the human body so as to capture images of organ tissues in the body; the control box comprises a feature processing unit, a mechanical arm driving control unit, an inner needle control unit and an air bag control unit, the feature processing unit is used for segmenting, recognizing and classifying captured images, the mechanical arm driving control unit is used for driving the mechanical arm to move, and the inner needle control unit is used for controlling the position, angle and depth of an inner needle assembly or a catheter; the airbag control unit is used for controlling air pressure of airbags; according to the method, the time of characteristic analysis of clinical pathology can be shortened, and the reliability is improved.