Enhanced x-ray angiography analysis and interpretation using deep learning

增强的X射线血管造影分析， 使用深度学习进行解释 美国每年进行超过100万次诊断性X射线血管造影，以指导冠状动脉疾病的治疗。 动脉疾病（CAD），花费超过120亿美元。尽管是临床护理标准，但目视判读 容易出现观察者间和观察者内的变化。最近作为NHLBI支持的前瞻性多中心研究的一部分， 影像学研究评价胸痛（PROMISE）试验，我们的研究小组表明，心脏病学家， 超过19%的阻塞性CAD血管造影片（血管狭窄超过50%）被误解。考虑到 血管造影解释的发展治疗计划，降低准确性可能会导致不必要的 结果不佳，增加了我们医疗保健系统的成本。潜在的影响是巨大的，因为 仅在美国，将解释准确性提高1%，每年就可以使超过10，000名患者受益。 因此，我们的团队正在开发一种由深度学习驱动的X射线血管造影分析系统（DeepAngio） 技术，以提高医生的解释。在第一阶段，超过1,000张血管造影片的PROMISE数据集被 用于构建我们基于卷积神经网络（CNN）的深度学习模型。我们达到了0.89的面积 在接收操作特征（AUROC）下识别图像中的阻塞性CAD 评分的地面实况（超过我们提出的>0.85 AUROC的第一阶段里程碑）。 现在在第二阶段，我们提出了一个创新的图像学习管道，将解剖和时空 来自视频序列的信息（类似于心脏病专家阅读器）。完整的端到端X射线血管造影视频 将在一个新的10，000例患者血管造影片队列中开发和测试处理管道， 分级异常CAD。我们基于补丁的帧分析模型将推进到基于CNN全帧的 血管造影视图的分类（左心脏对右心脏）和冠状血管（LAD，LCx， RCA）。递归神经网络（RNN）支持的多帧分析方法将使我们的 模型与动态时间信息，以准确地估计病变的存在。我们第二阶段的目标是 提高阅读特异性，并将我们的I期概念验证研究结果转化为临床验证。 有意义的工具。由一组介入心脏病专家进行的多阅片人、多病例评价， 在没有DeepAngio预测的情况下，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。

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.