374 results about "Pathology diagnosis" patented technology

The invention provides a multi-mode prostate cancer biochemical recurrence risk layering prediction system based on artificial intelligence. Based on an Xgboost framework, a postoperative patient pathological panoramic pathological section scanning image is analyzed through end-to-end, multi-scale, multi-center and large-sample analysis, pathological information is utilized to the maximum extent, meanwhile, the prognosis risk of a patient can be evaluated more comprehensively in combination with clinical indexes such as CAPRA-S scores, and the method has obvious advantages compared with a traditional model. The method aims at better fitting the use scene of a hospital, the risk of prostate cancer recurrence of a patient is more efficiently and accurately predicted by fusing pathological section features and clinical features after a radical operation, and the risk of recurrence of the patient within 3 years and longer time after the radical operation can be accurately predicted. And an interpretable module is further combined to assist a doctor to interpret a result, so that precise layering and personalized follow-up visit of the BCR risk of the prostatic cancer patient are realized, the risk of excessive treatment and missed diagnosis is reduced, and the method has a good application prospect.

A colorectal lesion multi-modal classification method based on pathological attention and multi-instance learning constructs an efficient automatic diagnosis model by fusing visual features and a textual prototype defined by pathology experts. The method comprises the following steps: collecting histopathological image data, segmenting the histopathological image data into standardized image blocks, and extracting visual features by using a pre-training feature extraction network after color standardization and noise processing; a multi-instance learning framework and a pathological attention mechanism are combined, feature space distribution of a text prototype is adjusted in a self-adaptive mode through a dynamic prototype optimization module, and optimization targets of visual clustering and cross-modal semantic alignment are balanced by adopting a gradient perception double-loss dynamic weighting strategy; and after the model is trained in stages, the generalization performance is verified in an external data set. According to the method, the classification precision is remarkably improved, the method can adapt to dyeing difference and tissue heterogeneity without pixel-level labeling, the accuracy rate in cross-center verification is superior to that of an existing reference model, and the efficiency of pathological diagnosis is greatly improved.

The system and method for processing a digital pathology image using a machine learning model that includes a self-supervised hierarchical Vision Transformer (ViT) configured to perform unsupervised clustering with multiple classification tokens. The method includes receiving a digital pathology image that depicts a tissue slice stained with histological dyes. The digital pathology image may be processed to generate a result comprising multiple predicted classifications of individual patches of the digital pathology image. The result is generated by a machine-learning model using a self-supervised hierarchical Vision Transformer (ViT) that may further comprise a multi-head self-attention module configured to predict a crosspatch relevance metric using an attention mechanism for each individual patch in the digital pathology image thereby assigning the individual patches to a cluster based on the crosspatch relevance metrics.

Presented herein are systems, methods, and non-transient computer readable media for determining predicted response scores of subjects. A computing system may identify a first feature set for a first subject to be administered with immunotherapy to address a condition. The first feature set may include one or more of: (i) a first radiological feature identified in a tomogram of a section associated with the condition in the first subject, (ii) a first immunohistochemistry (IHC) feature derived from an image of a sample associated with the first subject, and (iii) a first genomic feature obtained from gene sequencing of the first subject for genes associated with the condition. The computing system may apply the first feature set to a model. The computing system may determine, from applying the first feature set to the model, a predicted score identifying a response to the immunotherapy to be administered to the first subject.

The invention discloses bifidobacterium breve and application thereof in relieving Alzheimer's disease, and belongs to the technical field of probiotics. The preparation method comprises the following steps: separating 18 bifidobacterium strains from feces of children aged 0-3 years old, analyzing and identifying the 18 bifidobacterium strains including bifidobacterium longum, bifidobacterium breve and bifidobacterium animalis, and screening out the bifidobacterium breve LE4 with potential Alzheimer's disease relieving capacity through in-vitro oxidation resistance and LPS (Lipopolysaccharide) removal capacity. The method comprises the steps of behavioral experiments (nesting experiments, open field experiments and water maze experiments), histopathologic analysis, molecular biological detection and the like. The multi-target AD intervention effects of improving the cognitive function, reducing A beta deposition and tau protein phosphorylation, relieving neuroinflammation, enhancing synaptic plasticity, repairing intestinal barriers, adjusting intestinal flora and metabolites and the like of the LE4 strain are comprehensively evaluated, and a new strategy is provided for promoting AD micro-ecological treatment.

The invention relates to the field of medical informatization, in particular to a pathology laboratory ISO15189 intelligent face-to-face inspection auxiliary system based on a knowledge graph, and is applied to the quality management and standard authentication process of a pathology laboratory. According to the method, automatic mapping of the ISO15189 standard terms and pathological quality inspection key points is realized by constructing the knowledge graph in the pathology field and combining a natural language processing technology, so that the inspection efficiency is greatly improved; the system automatically generates a meeting inspection report meeting the standard, and shortens the document arrangement time from several days to several hours; meanwhile, the risk monitoring module collects service flow data, carries out risk assessment and early warning, and ensures that the inspection process is compliant. The application of the knowledge graph technology effectively overcomes the limitation of the traditional method in understanding the terminology semantics, and enhances the quality management and inspection preparation work of the pathology laboratory.

The invention relates to the technical field of pathological image analysis and mining, and particularly discloses an ovarian cancer subtype classification method based on prototype learning and multi-view deep embedding clustering, and the method comprises the following steps: S1, collecting a tissue pathological image of an ovarian cancer patient and a corresponding full-view digital pathological image; and S2, generating a multi-view data set. According to the ovarian cancer subtype classification method based on prototype learning and multi-view deep embedding clustering, the problem that in the prior art, patch-level labels are generally lacked in the field of multi-instance pathological images, so that many natural image processing methods cannot be applied to the field of pathological images is solved. A ResNet backbone network is used for extracting features of pathological images under the maximum magnification, a small number of pathology prototypes are introduced to guide deep embedded clustering through pathology expert priori knowledge, a pathology image spectrogram is introduced to serve as a reference view, and the accuracy and stability of clustering are enhanced.

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 relates to the field of multi-modal learning, in particular to a colorectal cancer drug chemotherapy reaction prediction system and a storage medium, and a computer program in the storage medium executes the following steps: constructing a PDO model and a PDOX model based on tumor cells; obtaining a standardized median inhibitory concentration value and a standardized relative tumor proliferation rate of the colorectal cancer patient by utilizing the PDO model and the PDOX model; based on the obtained medical record and pathological examination of the colorectal cancer patient, obtaining the age, ASA score, Ki-67 index, combined positive score and clinical outcome of the patient, and further constructing a sample set in combination with a standardized median inhibitory concentration value and a standardized relative tumor proliferation rate; and constructing a model for predicting the chemotherapy reaction of the colorectal cancer drug on the basis of multi-factor logistic regression, training and evaluating the model by using the sample set, and performing chemotherapy guidance on a to-be-treated colorectal cancer patient. The problems that in the prior art, chemotherapy reaction prediction of the colorectal cancer patient is not accurate enough and low in efficiency are solved.

The present invention relates to a cognitive computing-assisted clinical decision support system designed to enable personalized neurological rehabilitation. The system acquires structured user data across clinical, anatomical, radiological, etiological, pathological, and rehabilitation domains to create individualized profiles. These profiles are mapped against a repository of historical cases using analog matching and similarity scoring to generate stratified, evidence-based rehabilitation recommendations. Real-time monitoring of rehabilitation progress is performed using global recovery and function outcome indicators, allowing for dynamic adjustment of treatment plans. Clinician intervention modules ensure safety, interpretability, and context-aware customization. The system incorporates a continuous feedback mechanism to refine future predictions and recommendations, making it increasingly adaptive over time. The invention improves rehabilitation outcome prediction accuracy, reduces recovery variability, and optimizes functional outcomes by transforming static rehabilitation models into intelligent, responsive, and personalized care pathways.

The invention discloses a personalized health management method and system based on an AI electronic medical record, and relates to the technical field of artificial intelligence medical treatment, and the method comprises the steps: analyzing an original electronic medical record to generate a personal health timeline; carrying out feature extraction on the health state evolution sequence, identifying key nodes, and carrying out pathological labeling according to a medical knowledge graph to form a health state evolution sequence with a pathological label; a risk assessment model is constructed, future disease risks are calculated based on the sequence, and a dynamic report is generated; making a personalized health management plan in combination with the living habits and genetic backgrounds of the users; during plan execution, user feedback and monitoring data are collected in real time, and plan content and strength are dynamically adjusted by using a reinforcement learning mechanism. According to the method, the medical interpretability of health state evolution is enhanced through pathological labeling, and dynamic closed-loop optimization of a management plan is realized through reinforcement learning.

Techniques of automated quality control for digital pathology whole slide images are presented. The techniques include obtaining a thumbnail image derived from a whole slide image of a pathology slide; determining whether the whole slide image includes an artifact in a first class of artifacts by providing the thumbnail image to an electronic neural network trained to detect artifacts in the first class of artifacts by analyzing a plurality of labeled training thumbnail images; generating a tissue mask representing tissue depicted in the thumbnail image; determining whether the whole slide image includes an artifact in a second class of artifacts by performing a comparison using the tissue mask; and providing an indication of whether the whole slide image includes an artifact in the first class of artifacts or an artifact in the second class of artifacts.

The invention discloses a depth physical parameter integrated network and method for lensless microscopic imaging, and the method comprises the steps: firstly building a physical forward model based on fractional Fourier transform, and quantizing a propagation distance into an adjustable fractional order parameter; secondly, constructing a multi-stage progressive network framework, wherein each stage comprises a physical model fidelity module and an image regularization updating module; a self-adaptive near-end network and a self-adaptive compression-excitation network are innovatively embedded in a regularization module, and real-time self-adaption of network parameters to imaging conditions is achieved; finally, end-to-end single-frame reconstruction is realized, complex amplitude information is directly recovered from a single intensity observation image, and retraining for different imaging parameters is not needed. According to the method, the reconstruction physical reliability, the noise robustness and the cross-scene generalization ability are remarkably improved, meanwhile, the lightweight real-time deployment requirement is met, and the method is suitable for lens-free microscopic imaging scenes such as biological living body observation and portable pathological diagnosis.

The present disclosure provides systems and methods for detecting anti-NMDAR autoantibodies based on the strong affinity of the anti-NMDAR autoantibodies to a plurality of non-random anti-NR1s coupled to a solid support. The present disclosure also provides methods of treatment for anti-NMDAR pathology by the therapeutic anti-NMDAR antibody ART5803. The present disclosure also provides methods and systems for screening and predicting potential responsiveness to ART5803 therapy.

The invention discloses the technical field of pathology and space transcriptomics, and particularly relates to a gene expression prediction method and system based on multi-modal comparative learning and a guidance mechanism. Cutting the histological slice image into image blocks according to space coordinates; according to the method, a local convolution branch and a global Transform branch are combined to extract image features, the image features are mapped to a shared potential space through projection, soft contrast, hard contrast and global consistency constraints are introduced into the space, and cross-modal alignment of an image modal and a gene expression modal is realized; an expression prediction head is introduced in the training stage, representation learning is directly guided by a regression signal, and the relation between feature learning and gene expression prediction is broken through; in the inference stage, k-nearest neighbor retrieval and a multi-distance weighted aggregation strategy are combined to infer a gene expression profile of an unknown position. According to the method, the accuracy and robustness of space gene expression prediction can be effectively improved, the tissue space heterogeneity structure is kept, and the method has high clinical application and scientific research and popularization value.

The invention relates to the technical field of medical image diagnosis, and discloses a radiation-pathological diagnosis evaluation method based on weak supervision cross-modal deep fusion. The method comprises the following steps: receiving case-level radiation image data and pathological section data, combining with a weak supervision consistency label, realizing cross-modal semantic alignment through a double-branch feature extraction network, and generating aligned radiation feature vectors and pathological feature vectors; based on the aligned feature vector, a cross-modal attention fusion mechanism is adopted to complete deep fusion, and a fusion feature vector is obtained; a consistency evaluation task is executed based on a multi-task learning framework, and a consistency classification result, an inconsistency attribution result and a risk area positioning result are output; and based on the evaluation result, generating a visual diagnosis report through an interpretability analysis model. According to the method, cross-modal data can be effectively fused under a weak supervision condition, the accuracy and interpretability of diagnosis consistency evaluation are improved, and clinical data annotation requirements are met.

The invention relates to the technical field of biology and new medicine, in particular to a preparation method, an identification method and a histopathologic analysis method of a rat uterus scar diverticulum model. The preparation method of the rat uterus scar diverticulum model comprises the following steps: sequentially simulating rat pregnancy by using female progestational hormone, and infecting a uterus incision by using group B streptococcus (GBS) to obtain the rat uterus scar diverticulum model. The preparation method comprises the following steps: constructing a pseudopregnant rat model; preparing a GBS bacterial solution with a certain concentration, and immersing 3-0 silk threads with the length of about 1cm into the bacterial solution for 24 hours for later use; an incision with the length of about 2 cm is formed in the uterus on the two sides of the rat, and the incision is sutured discontinuously by an absorbable line. The simulated pregnant mouse is used, the influence of uterine hyperemia during human delivery and hormone change after female progestational hormone withdrawal on the uterine microenvironment is met, the formation of the human uterine scar diverticulum simulated by using the group B streptococcus infection incision is related to cesarean delivery incision infection, and therefore the success rate of the obtained model is high.

A method for assessing nonalcoholic steatohepatitis (NASH) cirrhosis in a liver biopsy sample includes extracting, from the liver biopsy sample, image data indicative of one or more histopathological features, wherein the one or more histopathological features comprise septa and / or nodules and / or fibrosis, and analysing the extracted image data, using a machine learning model trained to assess the one or more histopathological features, to determine a degree of NASH cirrhosis. Training the machine learning model includes providing a plurality of training samples and a plurality of validation samples, each sample comprising a graded liver biopsy sample; quantifying parameters of the one or more histopathological features from image data of each of the training samples; selecting a subset of quantified parameters of the one or more histopathological features; constructing a model for assessing the one or more histopathological features from the subset of quantified parameters; and validating the constructed model using the validation samples.

The invention relates to the technical field of medical detection and spectral analysis, and discloses a spectrum detection method for microorganisms on a wound surface based on knowledge data dual-drive envelope fusion. The method comprises the following steps: firstly, acquiring a multi-band near-infrared spectrum signal of a wound surface through a fiber optic spectrometer, and carrying out normalization and smooth filtering pretreatment; analyzing optical characteristic parameters based on a diffuse reflection theory model, constructing a knowledge-driven characteristic sample space, extracting spectral high-order characteristics by using a one-dimensional deep convolutional neural network, and constructing a data-driven characteristic sample space; then generating a grain envelope sample through a double-flow feature fusion mechanism; and finally, constructing a lightweight dual-task classification model based on the particle envelope sample, and realizing rapid detection of fungal infection of the wound surface and identification of seven common bacteria. According to the method, the interpretability, the recognition precision and the robustness of characteristic pathology are considered, and an efficient and reliable technical scheme is provided for early clinical diagnosis of wound infection.

The invention discloses an IgA nephropathy prognosis method, system and device based on multi-modal data, and belongs to the technical field of image data processing, the method comprises the following steps: collecting historical data including clinical data and an IgA immunofluorescence map; based on a visual identification method, pathological features are extracted from the IgA immunofluorescence image; screening clinical characteristics; based on a machine learning method, the training set is trained according to pathological features and clinical features, a prognosis model is obtained, and the prognosis model is used for IgA nephropathy prognosis. On the basis of a computer vision method, pathological features are extracted from an IgA immunofluorescence map, and the prognosis of the IgA nephropathy is predicted in combination with clinical features, so that automatic prediction is facilitated, mistakes and omissions caused by artificial naked eye recognition are avoided, stable prediction ability is expressed, and multi-modal data fusion reflects a gain effect on long-term prognosis prediction.

Provided herein are compounds that promote targeted degradation of IKZF1, IKZF2, GSPT1, and / or CK1a, proteins whose activities are implicated in the pathology of certain cancers (e.g., acute myeloid leukemia). Also provided are pharmaceutical compositions comprising the compounds. Also provided are methods of treating cancer, and methods of promoting the degradation of IKZF1, IKZF2, GSPT1, and / or CK1a in a subject or biological sample by administering a compound or composition described herein.

Systems and methods relate to processing digital-pathology images. More specifically, aspects of the present disclosure are directed to accessing a whole-slide image depicting a slice of specimen, defining a set of tiles within at least part of the whole-slide image, generating one or more artifact prediction metrics by applying artifact detection to each tile of the set of tiles, wherein each of the one or more artifact prediction metrics corresponds to a predicted level of image quality of part or all of the whole-slide image, generating a quality heat map image corresponding to the whole-slide image, wherein the quality heat map image is based on the one or more artifact prediction metrics, and outputting the quality heat map image.

Systems, apparatuses, and methods to generate and present personalized animations based on patient dental records and information. Embodiments obtain dental images and related data for at least one dental object and use machine learning models to detect features including pathological, non-pathological, bone levels and anatomical structure. The data is used to generate a visual representation of the natural progression of one or more dental pathologies and the corresponding relationship(s) with the anatomy of the tooth. By illustrating the progression through visual means, the approach may be used to show how untreated diseases can impact different anatomical features, and thereby emphasize the importance of timely treatment.

An oncological foundation model is trained with broad, multimodal data to make predictions concerning a variety of different types of cancers. For example, the foundation model may make use of medical images drawn from radiology and pathology, as well as immunohistochemistry data; the presence or absence of biomarkers for particular diagnoses; patient history data; patient demographic data; and other forms of medical data. When using medical images, whole medical images as well as feature sets derived from the medical images may be used. The foundation model may have both causal predictive abilities as well as generative abilities.

Techniques for classifying, using a deep learning model, histopathological whole slide images (WSIs) as comprising images of cancerous or non-cancerous tissue and / or as comprising images of cancerous tissue having a genetic mutation or not having a genetic mutation are described herein. The techniques include at least one processor configured to instantiate a container-based processing architecture to train and / or use the deep learning model to process and classify at least one WSI. In some embodiments, a treatment may be selected and administered based on a classification result obtained from the deep learning model.

The invention discloses a stomach cancer postoperative survival prediction method and system based on machine learning, and belongs to the technical field of medical worker crossing and medical worker combination. According to the technical scheme, the method comprises the following steps: acquiring clinical data of a gastric cancer patient, wherein the clinical data comprises demographic characteristics, tumor pathology characteristics, operation related parameters and laboratory detection indexes; filling missing values in the clinical data by using an iterative random forest missing value filling method based on mutual information weighting; on the basis of the filled data, a feature subset with the most information content for postoperative three-year survival prediction is screened out through a dual feature selection strategy; training a machine learning model by using the feature subset so as to predict the survival risk of the gastric cancer patient in three years after operation; and outputting a prediction result. The method has the beneficial effects that a plurality of key challenges from data preprocessing, feature engineering and model construction to interpretability and clinical application are systematically solved, and an accurate, reliable, transparent and practical gastric cancer postoperative survival prediction solution is finally formed.

The invention discloses a simulation image generation method, device and system and a storage medium. The simulation image generation method can comprise the following steps: acquiring a scanning image of a frozen section of the diseased tissue; processing the scanning image by using a pre-trained diffusion model to generate a simulation image of the paraffin section; wherein the diffusion model is embedded into a feature space mapping function to encode clinical feature information of the diseased tissue, and image features related to the scanned image are fused by using a multi-end attention mechanism. According to the method, the diffusion model is used for processing the frozen section image of the lesion tissue, and the realistic restoration of the paraffin section can be obtained, so that the accuracy of intraoperative frozen pathological diagnosis is improved.

Segmentation or other classification of digital pathology images with a deep learning model allows for sophisticated spatial features for cancer diagnosis to be extracted in an automated, fast, and accurate manner. A tiered analysis of tissue structure based in part on deep learning methods is provided. First, tissues depicted in a digital pathology image are segmented into cellular compartments (e.g., epithelial and stromal compartments). Second, the heterogeneity in the different cellular compartments are examined based on a clustering algorithm. Tissue can then be characterized in terms of inertia (or other spatial measures or features), which can be used to recognize disease. In some instances, multidimensional inertia (i.e., inertia computed in different cellular compartments or clustered components) can be used as an indicator of disease and its outcome.

Techniques for using combination stain types for machine learning models for digital pathology are described herein. In an example, a system accesses a first image of a sample comprising an immunohistochemistry (IHC) stain for a biomarker. The system accesses a second image of the sample comprising a hematoxylin and eosin (H&E) stain for nuclei. The system can segment tissue regions in the one or more first images and the second image, partitions the tissue regions in the one or more first images and the second image, and extracts features from the set of tiles using a feature extractor. The system can generate, by a machine-learning model, an output classification indicating a first phenotype based on the features extracted from the set of tiles. The machine-learning model can include one or more classifiers and an aggregation model that provides an aggregated output for the set of tiles.

A system and method for performing virtual birefringence imaging and virtual staining (e.g., Congo red) of label-free human tissue is disclosed to show that a single trained neural network can rapidly transform autofluorescence images of label-free tissue into brightfield and polarized microscopy images, matching their histochemically-stained versions. Blind testing with quantitative metrics and pathologist evaluations on cardiac tissue showed that the virtually stained polarization and brightfield images highlight amyloid patterns in a consistent manner, mitigating challenges due to variations in chemical staining quality and manual imaging processes in the clinical workflow.