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

• 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
• 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

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.

增强的X射线血管造影分析和 使用深度学习的解释 每年在美国进行超过100万个诊断X射线血管造影，以指导冠状动脉治疗 动脉疾病（CAD），耗资超过120亿美元。尽管是临床护理标准，但视觉解释 容易发生和观察者内变异性。最近，作为NHLBI的一部分 用于评估胸痛（承诺）试验的成像研究，我们的研究小组表明心脏病学家 误解了超过19％的血管造影阻塞性CAD（大于50％的血管狭窄）。考虑到中心 对治疗计划制定的血管造影解释，精度降低可能导致不必要 结果不佳，我们的医疗保健系统成本增加。鉴于 仅在美国，每年增加1％的解释准确性就可以积极受益于10,000多名患者。 因此，我们的团队正在开发一个由深度学习驱动的X射线血管造影分析系统（Deepangio） 增强医师解释的技术。在第一阶段，超过1,000次血管造影的承诺数据集为 用于建立基于我们的卷积神经网络（CNN）深度学习模型。我们达到了0.89区域 在接收操作特征（AUROC）下，用于识别与专家的图像中的阻塞性CAD 得分的真相（超过了我们提出的I阶段里程碑> 0.85 AUROC）。 现在在第二阶段，我们提出了一条创新的图像学习管道，以结合解剖和时空 来自视频序列的信息（类似于心脏病学家读者）。全端到端X射线血管造影视频 处理管道将在新的10,000例患者血管造影的新队列中开发和测试， 分级异常CAD。我们的基于补丁的框架分析模型将推向CNN全帧全帧 血管造影视图的分类（左心与右心）和冠状血管的分割（LAD，LCX， 和RCA）。由递归神经网络（RNN）启用了多个框架分析方法，将为我们的 具有动态时间信息的模型，以准确估计病变的存在。我们的第二阶段目标是 提高阅读特异性并将我们的I期概念证明研究结果转化为临床 有意义的工具。一组介入心脏病专家的多阅读器，多案例评估 并且没有Deepangio预测，将评估临床可用性以改善冠状动脉狭窄估计。 从长远来看，我们希望心脏病专家与DeepAngio作为辅助工具的结合将改善 血管造影解释的临床准确性。