[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Ahmadi Amiri, 2023 - Google Patents

Detection and severity assessment of aortic stenosis using machine learning

Ahmadi Amiri, 2023

View PDF
Document ID
9757012803029479266
Author
Ahmadi Amiri S
Publication year

External Links

Snippet

Aortic stenosis (AS) is a valvular cardiac disease that results in restricted motion and calcification of the aortic valve (AV). AS severity is currently assessed by expert cardiologists using Doppler measurements from echocardiography (echo). However, this method limits …
Continue reading at open.library.ubc.ca (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10—Services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K2209/05—Recognition of patterns in medical or anatomical images
    • G06K2209/051—Recognition of patterns in medical or anatomical images of internal organs

Similar Documents

Publication Publication Date Title
Gilbert et al. Generating synthetic labeled data from existing anatomical models: an example with echocardiography segmentation
US10321892B2 (en) Computerized characterization of cardiac motion in medical diagnostic ultrasound
US8812431B2 (en) Method and system for medical decision support using organ models and learning based discriminative distance functions
KR20220102635A (en) Deep neural network systems and methods for improving prediction of patient endpoints using video of the heart
JP6885517B1 (en) Diagnostic support device and model generation device
Nurmaini et al. Accurate detection of septal defects with fetal ultrasonography images using deep learning-based multiclass instance segmentation
Hernandez et al. Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability
de Siqueira et al. Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review
Tajbakhsh et al. Guest editorial annotation-efficient deep learning: the holy grail of medical imaging
Ahmadi et al. Transformer-based spatio-temporal analysis for classification of aortic stenosis severity from echocardiography cine series
Lu et al. PKRT-Net: prior knowledge-based relation transformer network for optic cup and disc segmentation
Nie et al. Recent advances in diagnosis of skin lesions using dermoscopic images based on deep learning
US20230351593A1 (en) Automatic clinical workflow that recognizes and analyzes 2d and doppler modality echocardiogram images for automated cardiac measurements and grading of aortic stenosis severity
Qiao et al. SPReCHD: Four-chamber semantic parsing network for recognizing fetal congenital heart disease in medical metaverse
Laumer et al. Weakly supervised inference of personalized heart meshes based on echocardiography videos
Wen Automatic tongue contour segmentation using deep learning
Raina et al. Deep Learning Model for Quality Assessment of Urinary Bladder Ultrasound Images using Multi-scale and Higher-order Processing
Ahmadi Amiri Detection and severity assessment of aortic stenosis using machine learning
Li et al. A task-unified network with transformer and spatial–temporal convolution for left ventricular quantification
Zhou et al. Uncertainty-aware incomplete multimodal fusion for few-shot Central Retinal Artery Occlusion classification
Ye et al. Artificial intelligence-based echocardiogram video classification by aggregating dynamic information
Sanjeevi et al. Deep learning supported echocardiogram analysis: A comprehensive review
Feng et al. A Bayesian network for simultaneous keyframe and landmark detection in ultrasonic cine
Feng Fusing Multiple Information Sources for Predictive Cardiac Modeling
US20240374234A1 (en) Automatic clinical workflow that recognizes and analyzes 2d and doppler modality echocardiogram images for automated cardiac measurements and grading of mitral valve and tricuspid valve regurgitation severity