26 results about "Clinical pathology" patented technology

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 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.

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.

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 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 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.

The invention discloses application of solamargine in preparation of a medicine for preventing and / or treating cholestatic liver diseases, and belongs to the field of biological medicines. By simulating a classical Mdr2- / -mouse model of clinical cholestasis pathology, the solamargine is researched and proved to treat cholestatic liver injury by improving hepatocyte injury, bile duct dysfunction and bile metabolism disorder, and the solamargine shows good application potential in clinical treatment of cholestatic liver diseases.

The utility model relates to the technical field of clinical pathological diagnosis, in particular to a pathological specimen cutting knife for clinical pathological diagnosis, which comprises a knife body arranged in a cutting groove on a cutting box, a baffle plate and two push plates, the knife body comprises a knife back and a knife edge, the baffle plate is fixed on one side of the knife back, and the two push plates are respectively fixed on two sides of the bottom of the knife back. Two positioning blocks are fixed at one end in the cutting groove, and residues after specimen cutting are prevented from falling into the cutting groove through a baffle; the pushing device is located on one side of the cutting groove, and the pushing device abuts against the knife back; after a specimen is cut by the blade, specimen residues slide to the outer side of the cutting groove through the baffle, and when the cutter body is replaced through the pushing device, the push plate at the bottom of the cutter back pushes out the residues in the cutting groove, so that the situation that insertion of subsequent bodies is affected due to the fact that the specimen residues of the cutter fall into the cutting groove is prevented.

The invention relates to the technical field of medical image processing. The invention discloses a habitat imaging-based gastric cancer neoadjuvant chemotherapy curative effect prediction method and a habitat imaging-based gastric cancer neoadjuvant chemotherapy curative effect prediction system, and the method comprises the following steps: S1, obtaining pre-treatment CT and clinical pathology information, and marking the maximum tumor area and upper and lower layers; s2, dividing the marked region into habitat sub-regions with different biological characteristics through habitat imaging; and S3, performing prognosis analysis on the habitat subregion, screening independent prognosis features related to survival, and generating a habitat subregion image. And S4, constructing a joint attention model, inputting images of the marked area and the habitat subarea, predicting the curative effect and the total lifetime, and obtaining a survival score. And S5, integrating clinical pathological information and survival scores, performing prognosis analysis, constructing a column graph, and evaluating correlation. After a CT image label before treatment is obtained, habitat imaging is used for dividing subareas, related images are generated, the model is input in a combined mode, tumor heterogeneity is revealed, and the curative effect is predicted.

The invention provides biomarkers, methods involving their use, and a diagnostic kit that enables the distinction between muscle-invasive bladder cancer (MIBC) and non-muscle- invasive bladder cancer (NMIBC) which represents a critical step in the diagnosis and treatment of bladder cancer. This kit, based on widely used and cost-effective immunohistochemistry (IHC) techniques in pathology practice, comprises an effective biomarker panel for distinguishing between MIBC and NMIBC. This invention is suitable for routine use in clinical pathology practice, enabling better guidance of patient treatment processes and more accurate determination of disease progression.

The invention provides an artificial-intelligence–based platform for automated analysis of Pap smear slides. The system integrates preprocessing, dual deep learning models (CNN-Transformer and EfficientNet-LSTM), and reporting modules to classify cytological images into diagnostic categories including NILM, ASC-US, LSIL, HSIL, and SCC. The platform includes interpretability outputs, secure data management, and active learning capabilities, enabling scalable, accurate, and transparent cervical-cancer screening across diverse laboratory settings. The platform is suitable for implementation in clinical pathology laboratories, telemedicine networks, and population-level screening programs. It allows rapid, reproducible, and objective evaluation of cytology slides, improving throughput while reducing reliance on manual slide examination. The invention is deployable on local servers, private clouds, or public cloud infrastructures and is adaptable to various cytology specimens. Adoption of this system enhances early detection of precancerous lesions, reduces inter-observer variability, and facilitates integration with laboratory information systems. It also supports continuous learning and adaptation to evolving imaging protocols. The invention provides multiple advantages over existing methods: (1) Dual-model architecture combining CNN-Transformer and EfficientNet- LSTM ensures robust classification across both dense and sparse cellular regions. (2)Preprocessing and adaptive tiling improve feature extraction from variable-quality images. (3)Interpretability modules enhance clinician trust and facilitate regulatory compliance. (4) Active learning allows continuous improvement based on expert feedback. (5) Modular deployment supports both local laboratory and cloud-based operation. (6) Data security features meet international standards for privacy and encryption. (7) High scalability enables screening of large slide volumes without compromising accuracy. The invention provides a clinically relevant, efficient, and technologically advanced solution for automated cervical-cancer screening, applicable in diverse healthcare environments and adaptable to future cytology domains.

The invention provides a detection kit for diagnosing colorectal cancer and application thereof, blood samples of 100 colorectal cancer patients diagnosed by clinical pathology and / or cytology are compared with blood samples of 100 healthy control people, a subject work mapping curve is described, and the curve and the area below the curve are calculated. The kit provided by the invention has the sensitivity of 100%, the specificity of 83.0%, the accuracy of 91.5% and the AUC of 0.911 when being used for detecting blood samples of colorectal cancer patients and healthy control people, so that the detection kit can be used for distinguishing the colorectal cancer patients and the healthy control people and has good accuracy and sensitivity, and therefore, the kit can be used for detecting the colorectal cancer patients and the healthy control people. The invention provides a kit which is based on blood metabolites and can be used for early colorectal cancer detection.

According to the detection kit for diagnosing the non-small cell lung cancer and the application, blood samples of 100 non-small cell lung cancer patients diagnosed by clinical pathology and / or cytology are compared with blood samples of 100 healthy control people, a subject working correspondence curve is depicted, the curve and the area below the curve are calculated, and the non-small cell lung cancer is diagnosed. The kit provided by the invention has the sensitivity of 99.0%, the specificity of 86.0%, the accuracy of 92.5% and the AUC of 0.940 when being used for detecting blood samples of non-small cell lung cancer patients and healthy control people, so that the detection kit can be used for distinguishing the non-small cell lung cancer patients and the healthy control people and has good accuracy and sensitivity, and therefore, the kit can be used for detecting blood samples of the non-small cell lung cancer patients and the healthy control people. The invention provides a kit which is based on blood metabolites and can be used for early non-small cell lung cancer detection.

The application belongs to the technical field of medical image processing and artificial intelligence, and discloses a colorectal cancer liver metastasis risk assessment method and system based on multi-modal fusion. The method comprises the following steps: obtaining preoperative abdominal computed tomography (CT) images and clinical pathological data of a patient; pre-processing the CT images, segmenting a three-dimensional region of interest, and extracting radiomics features, and standardizing the clinical data; inputting the radiomics features into a first prediction model to obtain an imaging score, and inputting the clinical data into a second prediction model to obtain a clinical score; based on a preset fusion strategy, the two scores are weighted and fused, and a comprehensive risk score of postoperative liver metastasis of the patient is output as auxiliary assessment information. The application can accurately assist in assessing the risk of colorectal cancer liver metastasis after surgery by non-invasively and efficiently integrating CT image quantitative features and clinical information, and provides an objective decision-making tool for clinicians to develop individualized diagnosis and treatment plans and follow-up strategies.

The application provides a clinical application level pathological large model construction method and system based on weak supervision learning, comprising: respectively performing weak supervision training on each historical clinical pathology image, adjusting upstream learning parameters of a large model according to a training result and constructing an upstream task of the large model, respectively performing segmentation decoding on each weak supervision training result, determining downstream learning parameters of the large model according to a segmentation result and constructing a downstream task of the large model, acquiring upstream fine features and downstream fine features, constructing an upstream and downstream collaborative task of the large model, combining the upstream task of the large model and the downstream task of the large model to generate a pathological large model, inputting a current clinical pathology image into the pathological large model to perform pathological information recognition, obtaining a plurality of pathological recognition labels corresponding to a patient, and generating a pathological report of the patient, so that the computational complexity in processing a high-resolution image is reduced, the collaborative mechanism between different tasks in the large model is optimized, and the pathological recognition technology can be widely applied.

A memory storing at least one instruction for multimodal deep learning based on modality-specific weight learning that receives image data and clinical pathology data according to an embodiment as input and predicts whether a target patient will relapse; and a processor that performs an operation according to said instruction, wherein the processor includes: an image feature extraction module that receives a plurality of image data and extracts multi-stage features; a non-image embedding module that receives a non-image variable including at least one of clinical, numerical, historical, and environmental information and generates a clinical embedding; a dimension transformation module that includes a reduction layer that compresses image features to a lower dimension and an expansion layer that expands non-image embeddings to a higher dimension to resolve the dimension imbalance between the image features and the non-image embeddings; an adaptive modality attention module (AdaMod) that calculates the contribution of each modality on a sample basis for the transformed image feature and non-image embedding vectors and generates a personalized fusion representation by applying normalized modality-specific weights; and a fusion and classification module that performs a final prediction by combining the feature vectors to which the attention weights are applied.

The application discloses a liver cancer treatment effect prediction model construction method based on IFITM3, relates to the technical field of biological medicine, and has the technical points that an IFITM3 whole-process standardization detection system from tumor tissue pretreatment, immunohistochemical staining, chromatin control to digital imaging and positive area quantification is established; the system can eliminate batch differences of immunohistochemical detection by fixing reagent parameters, a staining process and quality control rules; the stability and repeatability of marker quantitative results are ensured by unified ROI selection, quantification algorithms and feature summary rules; meanwhile, the whole process is completely compatible with conventional detection operation of a clinical pathology department, and high-end detection equipment does not need to be newly added, so that standardized operation specifications are provided for clinical transformation of the model.

The application provides application of cell balls in construction of human breast cancer immunohistochemical quality control products, and belongs to the technical field of pathological examination. The application solves the problems of scattered distribution of tumor cells in the immunohistochemical quality control products in the prior art, lack of morphological structure, and significant difference from human breast cancer tissue samples to be detected; the quality control sheet prepared by the application is more stable, can ensure that the tissue morphology on all used quality control sheets remains consistent, the quality control sheet can be mass-produced, and does not exist ethical problems, can provide breast cancer ER, PR, HER2 and Ki67 immunohistochemical quality control products for daily detection of clinical pathology departments, simplifies the procedure of setting immunohistochemical controls in clinical work, and only a single wax block can meet the quality control needs of human breast cancer immunohistochemical detection, is convenient and stable.

The invention provides a breast tumor and gland density ratio prediction adjuvant therapy method. The method comprises the following steps: S101, acquiring first breast X-ray image data, clinical pathology data and follow-up visit data; s102, performing preprocessing operation on the acquired mammary gland X-ray image data, and constructing an image data set; s103, constructing a mammary gland segmentation model, using the image data set for training and verification of the mammary gland segmentation model, and obtaining second mammary gland X-ray image data of the patient; s104, constructing a mammary gland diagnosis model, and using the mammary gland diagnosis model to diagnose the second mammary gland X-ray image data segmented by the mammary gland segmentation model; s105, calculating the density ratio of the breast tumor to the gland; s106, constructing a mammary gland prediction model, and predicting the deterioration probability of the mammary gland tumor of the patient in the future period; and S107, generating personalized treatment scheme suggestions for the patient. The problems that traditional breast tumor treatment depends on subjective experience and is lack of quantitative index guidance are solved, and the accuracy and effectiveness of a treatment scheme are improved.

The present invention is directed towards the application of toxicogenomic 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 may be obtained in the absence of any image data. A computer-implemented method of 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 configured to output a result indicative of the likelihood of the presence of a histopathological abnormality in the organ of the human or animal subject; and outputting the result.

The invention relates to the technical field of medical care informatics, and discloses a method and a system for constructing an endometrial cancer prognosis risk assessment model, and the method comprises the steps: obtaining multi-dimensional data containing clinical pathology, molecular typing, specific molecular characteristics and immune characteristics, training a machine learning model based on the multi-dimensional data, and constructing an endometrial cancer prognosis risk assessment model. And learning a complex relationship between the characteristics and the prognosis risk so as to construct a comprehensive and high-precision endometrial cancer prognosis risk assessment model. According to the endometrial cancer prognosis early warning system and method, the transformation of endometrial cancer prognosis from subjective and single qualitative evaluation to objective and multi-dimensional quantitative prediction can be achieved, high-risk patients can be objectively and accurately recognized, early-stage and active risk early warning is achieved through the integrated early warning system, and a reliable basis is provided for clinical individualized treatment decision making.

The invention discloses a non-small cell lung cancer postoperative recurrence evaluation model construction method based on circulating blood tumor DNA. The non-small cell lung cancer postoperative recurrence evaluation model construction method comprises the following steps: S1, defining a detected sample with at least one credible mutation as ctDNA positive; s2, screening patients whose ctDNA single detection is negative within m months after operation, and obtaining clinical pathology information and perioperative ctDNA detection results of the patients; s3, integrating the clinical pathological information in the step S2 and the ctDNA detection result as an initial feature set, and screening a final feature set related to the progress-free lifetime by using LASSO regression and an XGBoost algorithm; and S4, constructing a prognosis evaluation model based on the final feature set, and verifying the prognosis evaluation model through the external queue data set to evaluate the efficiency of the model. According to the invention, the sensitivity of single detection of the ctDNA after the lung cancer operation is greatly improved, the ctDNA is closer to clinical requirements, and practical and effective positive influences are provided for treatment decisions.

The invention relates to the technical field of clinical pathology, in particular to a diagnostic sampling and slicing device which is characterized in that two supporting frames are fixedly arranged on the front side and the rear side of the inner wall of an operation box respectively, a fixing seat is located between the two supporting frames, sliding grooves are formed in the opposite faces of the two supporting frames, and limiting assemblies are arranged in the sliding grooves in a sliding mode; the driving assembly is arranged in the operation box and located above the supporting frame. The multiple cutting sleeves are arranged on the driving assembly, and cutting pieces are arranged at openings in the right ends of the cutting sleeves. The multiple cutting sleeves are moved to the position above the fixing base through the driving assembly, then the samples are sliced through the cutting sleeves through the limiting assembly, the cut chippings and the samples are stored in the cutting sleeves, and later cleaning by operators is facilitated.