[go: up one dir, main page]

Betancur et al., 2018 - Google Patents

Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study

Betancur et al., 2018

View HTML @Full View
Document ID
13037712637949212949
Author
Betancur J
Commandeur F
Motlagh M
Sharir T
Einstein A
Bokhari S
Fish M
Ruddy T
Kaufmann P
Sinusas A
Miller E
Bateman T
Dorbala S
Di Carli M
Germano G
Otaki Y
Tamarappoo B
Dey D
Berman D
Slomka P
Publication year
Publication venue
JACC: Cardiovascular Imaging

External Links

Snippet

Objectives: The study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion deficit (TPD). Background: Deep convolutional neural networks trained with a large …
Continue reading at www.jacc.org (HTML) (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
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G06F19/321—Management of medical image data, e.g. communication or archiving systems such as picture archiving and communication systems [PACS] or related medical protocols such as digital imaging and communications in medicine protocol [DICOM]; Editing of medical image data, e.g. adding diagnosis information
    • 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
    • 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
    • G06F19/322—Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
    • 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
    • G06F19/327—Management of hospital data, e.g. scheduling of medical staff or operation rooms, measuring the quality or efficiency of medical staff
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • 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
    • G06Q50/22—Health care, e.g. hospitals; Social work
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/50—Clinical applications
    • A61B6/507—Clinical applications involving determination of haemodynamic parameters, e.g. perfusion CT
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/48—Diagnostic techniques

Similar Documents

Publication Publication Date Title
Betancur et al. Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study
Betancur et al. Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning
Otaki et al. Clinical deployment of explainable artificial intelligence of SPECT for diagnosis of coronary artery disease
Baskaran et al. Identification and quantification of cardiovascular structures from CCTA: an end-to-end, rapid, pixel-wise, deep-learning method
Pieszko et al. Deep learning of coronary calcium scores from PET/CT attenuation maps accurately predicts adverse cardiovascular events
Nakanishi et al. Machine learning adds to clinical and CAC assessments in predicting 10-year CHD and CVD deaths
Commandeur et al. Fully automated CT quantification of epicardial adipose tissue by deep learning: a multicenter study
Kusunose et al. A deep learning approach for assessment of regional wall motion abnormality from echocardiographic images
Betancur et al. Deep learning analysis of upright-supine high-efficiency SPECT myocardial perfusion imaging for prediction of obstructive coronary artery disease: a multicenter study
EP4161391B1 (en) Systems and methods for automated analysis of medical images
Vorontsov et al. Deep learning for automated segmentation of liver lesions at CT in patients with colorectal cancer liver metastases
Xie et al. The coronary artery disease–reporting and data system (CAD-RADS) prognostic and clinical implications associated with standardized coronary computed tomography angiography reporting
Cano-Espinosa et al. Automated Agatston score computation in non-ECG gated CT scans using deep learning
Hulten et al. Outcomes after coronary computed tomography angiography in the emergency department: a systematic review and meta-analysis of randomized, controlled trials
Azadani et al. Impact of early revascularization on major adverse cardiovascular events in relation to automatically quantified ischemia
Singh et al. Direct risk assessment from myocardial perfusion imaging using explainable deep learning
Lin et al. Artificial intelligence in cardiovascular CT: Current status and future implications
Argentiero et al. The applications of artificial intelligence in cardiovascular magnetic resonance—a comprehensive review
Singh et al. Deep learning for explainable estimation of mortality risk from myocardial positron emission tomography images
US20190108905A1 (en) System and method for processing medical image data
Eisenberg et al. Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT
Bhosale et al. Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review
Lai et al. DBT Masses Automatic Segmentation Using U‐Net Neural Networks
Joshi et al. Current and future applications of artificial intelligence in cardiac CT
Kay et al. Identification of high-risk left ventricular hypertrophy on calcium scoring cardiac computed tomography scans: validation in the DHS