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Enhanced x-ray angiography analysis and interpretation using deep learning

使用深度学习增强 X 射线血管造影分析和解释

基本信息

批准号：
10000961
负责人：
Ricardo Henao Giraldo
金额：
$ 70.25万
依托单位：
VIGILANT MEDICAL, INC.
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-07 至 2023-07-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10000961
关键词：
3-Dimensional Address Anatomy Angiography Anterior Architecture Area Cephalic Characteristics Chest Pain Classification Clinical Computer Vision Systems Computers Coronary Coronary Arteriosclerosis Coronary Stenosis Coronary Vessels Cost of Illness Data Data Set Development Diagnosis Diagnostic radiologic examination Engineering Evaluation Evaluation Studies Goals Gold Healthcare Systems Heart Image Institutional Review Boards Intervention Intraobserver Variability Lateral Lead Learning Left Lesion Methodology Modeling Morbidity - disease rate National Heart, Lung, and Blood Institute Network-based Neural Network Simulation Observational Study Outcome Pathway Analysis Patient-Focused Outcomes Patients Performance Phase Physicians Procedures Process Reader Reading Reporting Research Roentgen Rays Self-Help Devices Severities Specificity Stenosis Structure Systems Analysis Technology Testing Translating Trees Visual Visual Aid Work base clinically relevant cohort computer aided detection convolutional neural network coronary lesion cost deep learning deep neural network diagnostic accuracy group intervention image processing imaging study improved innovation novel prospective recursive neural network spatiotemporal standard of care tool treatment planning usability

项目摘要

Enhanced x-ray angiography analysis and interpretation using deep learning Over 1 Million diagnostic X-ray angiograms are performed annually in the US to guide treatment of coronary artery disease (CAD) and cost over $12 billion. Despite being the clinical standard of care, visual interpretation is prone to inter- and intra-observer variability. Recently as part of the NHLBI supported Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) trial, our research team showed that cardiologists misinterpreted over 19% of angiograms obstructive CAD (greater than 50% vessel stenosis). Given the centrality of angiographic interpretation to the development of a treatment plan, reduced accuracy can lead to unnecessary poor outcomes and increased costs to our healthcare system. The potential impact is significant given that increasing interpretation accuracy by 1% could positively benefit over 10,000 patients each year in the US alone. Thus, our team is developing an X-ray angiographic analysis system (DeepAngio) driven by deep learning technology to enhance physician interpretation. In Phase I, the PROMISE dataset of over 1,000 angiograms was used to build our Convolutional Neural Network (CNN) based deep learning model. We achieved a 0.89 Area Under the Receiving Operating Characteristic (AUROC) for identifying obstructive CAD in images with expert scored ground truth (exceeding our proposed Phase I milestone of >0.85 AUROC). Now in Phase II, we present an innovative image learning pipeline to incorporate anatomical and spatiotemporal information from video sequences (similar to a cardiologist reader). A full end to end X-ray angiography video processing pipeline will be developed and tested in a new cohort of 10,000 patient angiograms with normal and graded abnormal CAD. Our patch-based frame analysis model will advance to CNN full frame-based classification of angiographic views (left heart vs. right heart) and segmentation of coronary vessels (LAD, LCx, and RCA). A multiple frame analysis approach enabled by a Recursive Neural Network (RNN) will equip our model with dynamic temporal information to estimate lesion presence accurately. Our goal for Phase II is to improve reading specificity and translate our Phase I proof of concept research findings into a clinically meaningful tool. A multi-reader, multi-case evaluation by a group of interventional cardiologists interpreting with and without DeepAngio predictions will assess clinical usability to improve coronary stenosis estimation. In the long term, we hope the combination of a cardiologist with DeepAngio as an assistive tool will improve the clinical accuracy of angiographic interpretation.

增强x线血管造影分析和