711 results about "Pathology diagnosis" patented technology

Methods and systems utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analysis framework that identifies and quantifies biological properties / analytes from imaging data, and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach using imaging to examine underlying biology as an intermediate for assessing pathology provides numerous analysis and processing advantages over systems and methods configured to directly determine and characterize pathology from underlying imaging data.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties / analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties / analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Computational refocusing-assisted deep learning methods, apparatus, and systems are described. In certain pathology examples, a representative image is generated using a machine learning model trained with uniformly focused training images generated by a Fourier ptychographic digital refocusing procedure and abnormalities are automatedly identified and / or enumerated based on the representative image.

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.

An artificial intelligence engine for directed hypothesis generation and ranking uses multiple heterogeneous knowledge graphs integrating disease-specific multi-omic data specific to a patient or cohort of patients. The engine also uses a knowledge graph representation of ‘what the world knows’ in the relevant bio-medical subspace. The engine applies a hypothesis generation module, a semantic search analysis component to allow fast acquiring and construction of cohorts, as well as aggregating, summarizing, visualizing and returning ranked multi-omic alterations in terms of clinical actionability and degree of surprise for individual samples and cohorts. The engine also applies a moderator module that ranks and filters hypotheses, where the most promising hypothesis can be presented to domain experts (e.g., physicians, oncologists, pathologists, radiologists and researchers) for feedback. The engine also uses a continuous integration module that iteratively refines and updates entities and relationships and their representations to yield higher quality of hypothesis generation over time.

The invention belongs to the technical field of histopathology full-slice image analysis, and relates to a gene mutation prediction method and system based on a pathological report and full-slice cross-modal representation learning, and the method comprises the steps: obtaining a histopathology full-slice image and a data set of a corresponding pathological and molecular detection report, and carrying out the preprocessing and coding; performing word segmentation and feature extraction on the pathological diagnosis report and the molecular detection report; cross-modal representation learning is realized through reconstruction of pathological report features and comparative learning based on a category refinement queue; designing a loss function and training the model offline by using a sample data set with a label; and performing lung cancer gene mutation prediction on the histopathology full-slice image by using the trained model. According to the method, the defects that detailed annotations of histopathology full-slice image data are difficult to obtain and the single-modal information expression ability is insufficient can be overcome, and slice image level mutation tag classification is effectively completed by using a cross-modal information guide weak supervision method of a pathological report and a molecular detection report.

The invention provides an image real-time splicing transition method and device based on a pathological slide and a readable storage medium. The method and the device are innovated and improved for solving the problem that existing slide scanning digital splicing traces affect diagnosis. An overlapping area is reserved when adjacent original images are scanned, a weight transition template is generated by using a specific algorithm according to the distance between pixels and an overlapping boundary, and the transition smoothness is flexibly adjusted by controlling adjustable parameters. Overlapping region pixels and the corresponding templates are multiplied and accumulated to replace original data, overlapping region removing splicing is carried out, cyclic image-by-image processing is carried out, and traceless splicing is achieved. Through the optimization, the image definition and the film reading quality are remarkably improved, the misjudgment risk of doctors is greatly reduced, the pathological image analysis application range is powerfully expanded, clinical diagnosis and research are promoted to accurately and efficiently advance, powerful power is injected for pathology development, and the method has important value and wide application prospects in the technical field of medical images.

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.

Exemplary methods, systems, procedures, computer programs, kits, devices, and computer-executable code according to exemplary embodiments of the present disclosure can be provided for predicting cancer outcomes and treatment response. Thus, exemplary systems, methods, and computer-accessible medium can be provided that can extract at least one feature from a medical image of a tissue to provide an extracted image feature, extract at least one feature from digitized histopathology data of the tissue to provide an extracted histopathology feature, process the extracted image feature and the extracted histopathology feature to provide a multi-modal representation of the tissue, and process the multimodal representation of the tissue with a specific procedure to provide a prediction regarding a medical outcome of the tissue.

Systems and methods are provided for providing natural language decision support for pathology. A lower-dimensionality representation of each of a set of received pathology image is generated and a first set of tokens is generated from the representations of the set of pathology images by projecting the lower-dimensionality representations of the received pathology images to a same dimension as an embedding space of a large language model for text tokens or through multimodal blocks added to the large language model such as cross-attention. The large language model is trained on an instruction dataset complied from a plurality of pathology-related sources. A second set of tokens associated with a natural language prompt is received at the large language model. A response is determined from the first set of tokens and the second set of tokens at the large language model.

The invention belongs to the technical field of kidney histopathology and immunological evaluation, and particularly relates to a kidney pathology detection kit, a detection method and application, the detection kit comprises 37 rare metal element labeled monoclonal antibodies combined with antigens of in-situ tissue cells, and the detection method comprises the following steps: (1) collecting a sample to be detected, preparing a pre-dyed paraffin section; (2) mixing 37 monoclonal antibodies labeled by rare metal elements, and carrying out antibody staining on various antigen targets on the surfaces of cells in the in-situ tissue; (3) carrying out nucleus DNA staining on the stained tissue sample; and (4) detecting the stained tissue sample through an imaging mass spectrum flow system, and collecting original information data through laser spot firing. The method is an analysis method for fine grouping identification and relative quantification of structural cells, immune cells and pathological injury cells in the clinical nephropathy sample in-situ tissue. And auxiliary diagnosis information is provided for diagnosis, grading, treatment and prognosis detection of various clinical nephropathy.

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 discloses an esophageal cancer neoadjuvant chemotherapy and post-immunization pathology complete remission prediction system and method, and relates to the field of medical image processing, and the system comprises an image processing module which is used for carrying out the standardized processing of an input image, and segmenting a tumor region in the image; the feature extraction module is used for extracting voxel-level radiomics features from the tumor area; the feature screening module is used for screening out a plurality of voxel-level radiomics features with the most predictive capability; and the pathology prediction module is used for generating a comprehensive feature vector according to the segmented input image and the screened voxel-level radiomics features, and predicting the curative effect of the combination of neoadjuvant chemotherapy and immunotherapy according to the comprehensive feature vector. According to the technical scheme provided by the invention, the accuracy of predicting complete pathologic remission after neoadjuvant chemotherapy and immunotherapy of esophageal cancer can be improved, the defect that the heterogeneity in tumors cannot be considered in a traditional imaging omics technology is overcome, and the method has a relatively high application value.

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.

Viewer systems and related methods with user interactive user interfaces that provide a viewport with first and second digital images that allow a user to navigate an active one of the digital images which drives a viewing window over the other digital image to provide a view that moves in response to navigation of the active slide to allow a user to compare and select one of several candidate registrations to register the first and second slides for pathology or other medical review. The user can interact with the user interface to switch which image is the active images (e.g., a WSI or slide image), select an object in the digital image and direct the system to provide other digital images.

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.

A dyeing-free imaging method for a plasmon tissue section based on a generative adversarial network relates to the technical field of biomedical detection, and comprises the following steps: 1) recording the tissue section placed on a plasmon glass slide by using a high-resolution bright field microscope and passing through a Hamp; e, dividing the dyed tissue slices into a training set and a verification set in proportion; 2) amplifying the number of paired images in the training set to a plurality of times through data enhancement; 3) constructing a GAN model, and training the GAN model by using the paired image after data enhancement as a data set; and 4) based on the trained GAN model, performing virtual staining of the staining-free imaging image of the plasmon tissue section. According to the invention, the generated virtual dyeing image is visually and statistically compared with the real Hamp; the E-dyed image has high similarity, and provides pathological interpretable images and more effective discrimination information for pathologists. The time is remarkably shortened, the cancer diagnosis efficiency is improved, and a rapid and effective tissue slice analysis means is provided for clinic.

In one embodiment, a method, comprising: obtaining a correlation between the pre-operative ultrasound images and the post-operative histopathological images based on application of a fused image to a neural network; receiving post-operative ultrasound images; using the correlation to translate the post-operative ultrasound images to synthesized histopathology images; fusing the post-operative ultrasound images with the synthesized histopathology images; out-painting the synthesized histopathology images to a border zone remaining after the surgical procedure, the out-painting performed on a neural network; and displaying the out-painted, synthesized image with the post-operative ultrasound images.

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.

Systems and methods are provided for analysis of pathology data. Either a input data representing a pathology or a search query is received as an input and a first set of tokens is generated from the one of the input data representing a pathology and the search query from the input. The first set of tokens is matched to a second set of tokens at a multimodal fusion model trained on a pretraining dataset complied from a plurality of pathology-related sources. An output is provided based on the second set of tokens.

The invention discloses a high-resolution breast cancer HER2 scoring method based on deep learning. The high-resolution breast cancer HER2 scoring method comprises the following steps: constructing a full-slide image data set containing hematoxylin-eosin and HER2 staining; accurately marking by a pathological expert, and distinguishing normal, in-situ and invasive tumor areas; cutting, classifying and marking blocks with different sizes and magnification times; carrying out gray level conversion, binarization and morphological operation on the image blocks to extract a tissue region; designing a deep learning model combining a visual converter and Fourier transform, and optimizing feature extraction and frequency domain analysis; an optimal backbone network structure is selected through comparison; the model is trained, parameters are optimized, and the recognition and evaluation accuracy is improved; and finally integrating to an automatic scoring software system to realize automatic identification, HER2 protein expression evaluation and automatic scoring. The system is high in automation degree, the HER2 scoring process is simplified, the auxiliary diagnosis precision is improved, the workload of doctors is reduced, and pathologists are helped to concentrate on complex diagnosis tasks, so that the patient management effect is improved.

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.

A laboratory information system (LIS) is linked with a whole slide image (WSI) management system to build a pathology image management system that can efficiently manage and utilize a digitized pathology image. A scan notification notifying that an image of a glass slide is scanned by a scanner is delivered to the LIS from the WSI management system to form a WSI link in the LIS, so that the LIS is linked with the WSI management system. By linking two systems with each other can eliminate the problem of mixing up specimens, for example, and hence, a pathologist can easily grasp the status of a pathology sample for which the pathologist is responsible.