467 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.

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.

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.

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 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.

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.

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 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.

Systems and methods are provided for generating clusters of anomalous images. Histopathological samples are images at an associated imaging system to produce a plurality of images. A plurality of anomalous images are identified from the plurality of images at an anomaly detection system, having an associated latent space. The plurality of anomalous images are clustered to generate a plurality of clusters within a feature space defined by a set of classification features. The set of classification features include a feature derived from the latent space associated with the anomaly detection system.

A computer apparatus and method for identifying and visualizing tumors in a histological image and measuring a tumor margin are provided. A CNN is used to classify pixels in the image according to whether they are determined to relate to nontumorous tissue, or one or more classes for tumorous tissue. Segmentation is carried out based on the CNN results to generate a mask that marks areas occupied by individual tumors. Summary statistics for each tumor are computed and supplied to a filter which edits the segmentation mask by filtering out tumors deemed to be insignificant. Optionally, the tumors that pass the filter may be ranked according to the summary statistics, for example in order of clinical relevance or by a sensible order of review for a pathologist. A visualization application can then display the histological image having regard to the segmentation mask, summary statistics and / or ranking. Tumor masses extracted by resection are painted with an ink to highlight its surface region. The CNN is trained to distinguish ink and no-ink tissue as well as tumor and no-tumor tissue. The CNN is applied to the histological image to generate an output image whose pixels are assigned to the tissue classes. Tumor margin status of the tissue section is determined by the presence or absence of tumor-and-ink classified pixels. Tumor margin involvement and tumor margin distance are determined by computing additional parameters based on classification-specified inter-pixel distance parameters.

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 invention provides an examination system suitable for pathology teaching, which is based on a pathology database, combines with examination key points, automatically generates examination papers, answers and submits the examination papers based on identity information of users, and combines with a multimedia technology and a remote teaching means based on the pathology database. Pathology simulation practice operation is designed for online practice of a user, and a practice operation result is obtained; based on the test paper result and the practical operation result, performing pathological knowledge mastering evaluation on the user to obtain a user evaluation result; the problem that a traditional examination form is difficult to comprehensively evaluate learning results of students is solved, the examination system suitable for pathology teaching can provide diversified evaluation means, learning conditions of the students can be more comprehensively understood by comprehensively evaluating knowledge mastering degrees of the students, and meanwhile, by means of a multimedia technology and a remote teaching means, the learning results of the students can be comprehensively evaluated. More practical opportunities are provided for students, observation and analysis experience of the students on pathological specimens is increased, and practical operation ability is improved.

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 belongs to the technical field of biomedicine, and particularly relates to application of ceramide as a marker in prediction of hepatocellular carcinoma microvascular invasion risk. The invention finds that the preoperative plasma ceramide CER (d18: 1 / 16: 0) level can be used for predicting the MVI of HBV-HCC patients and evaluating the postoperative prognosis of the patients for the first time. The invention provides a preoperative non-invasive detection method based on plasma ceramide CER (d18: 1 / 16: 0), and trauma of traditional histopathology detection is avoided. According to the method, the occurrence risk of the MVI can be effectively reduced by simply detecting a blood sample of a patient. According to the method, the MVI risk of a patient is evaluated through a multi-factor prediction model, and a basis is provided for personalized treatment. The method is not only suitable for preoperative MVI risk assessment, but also can assist in making postoperative follow-up visit and treatment plans, and helps to reduce the postoperative recurrence rate and improve the survival rate of patients.

The invention belongs to the technical field of medical care informatics, particularly relates to a method and a system for constructing a refined model of cerebral apoplexy dysfunction based on an ICF theory, and aims to construct a refined model capable of accurately describing a dysfunction area of a cerebral apoplexy patient through dynamic optimization and a self-adaptive adjustment mechanism. In order to improve the accuracy and efficiency of dysfunction assessment, multi-modal assessment data, including pathological information, imaging data and biochemical indexes, of a patient is continuously collected and analyzed through a dynamic scoring mechanism, then, in combination with an ICF theory, feature parameters of a barrier region are automatically extracted and optimized through feature matching and a deep learning algorithm, and the accuracy and efficiency of dysfunction assessment are improved. According to the method, a three-dimensional model capable of accurately evaluating and positioning an obstacle area is constructed, and the model not only can update and adaptively adjust parameters in real time, but also can provide personalized rehabilitation suggestions and treatment schemes according to individual differences of different patients.

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.