Objectives

The purpose of this study was to establish whether an artificially intelligent (AI) system can be developed to automate stress echocardiography analysis and support clinician interpretation.

Background

Coronary artery disease is the leading global cause of mortality and morbidity and stress echocardiography remains one of the most commonly used diagnostic imaging tests.

Methods

An automated image processing pipeline was developed to extract novel geometric and kinematic features from stress echocardiograms collected as part of a large, United Kingdom-based prospective, multicenter, multivendor study. An ensemble machine learning classifier was trained, using the extracted features, to identify patients with severe coronary artery disease on invasive coronary angiography. The model was tested in an independent U.S. study. How availability of an AI classification might impact clinical interpretation of stress echocardiograms was evaluated in a randomized crossover reader study.

Results

Acceptable classification accuracy for identification of patients with severe coronary artery disease in the training data set was achieved on cross-fold validation based on 31 unique geometric and kinematic features, with a specificity of 92.7% and a sensitivity of 84.4%. This accuracy was maintained in the independent validation data set. The use of the AI classification tool by clinicians increased inter-reader agreement and confidence as well as sensitivity for detection of disease by 10% to achieve an area under the receiver-operating characteristic curve of 0.93.

Conclusions

Automated analysis of stress echocardiograms is possible using AI and provision of automated classifications to clinicians when reading stress echocardiograms could improve accuracy, inter-reader agreement, and reader confidence.

中文翻译：

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使用人工智能自动检测严重冠状动脉疾病的超声心动图

目标

本研究的目的是确定是否可以开发人工智能 (AI) 系统来自动化负荷超声心动图分析并支持临床医生解释。

背景

冠状动脉疾病是全球死亡率和发病率的主要原因，负荷超声心动图仍然是最常用的诊断成像测试之一。

方法

开发了一种自动图像处理管道，以从作为大型、基于英国的前瞻性、多中心、多供应商研究的一部分收集的应力超声心动图中提取新的几何和运动学特征。使用提取的特征训练了一个集成机器学习分类器，以在侵入性冠状动脉造影中识别患有严重冠状动脉疾病的患者。该模型在一项独立的美国研究中进行了测试。在一项随机交叉读者研究中评估了 AI 分类的可用性如何影响负荷超声心动图的临床解释。

结果

基于 31 种独特的几何和运动学特征的交叉折叠验证，在训练数据集中识别严重冠状动脉疾病患者的分类准确度达到了可接受的水平，特异性为 92.7%，敏感性为 84.4%。这种准确性在独立的验证数据集中得到了保持。临床医生使用 AI 分类工具将阅读者间的一致性和信心以及疾病检测的灵敏度提高了 10%，以达到 0.93 的接受者操作特征曲线下面积。

结论

当读取压力超声心动图可以提高准确性、读者之间的一致性和读者信心时，可以使用人工智能对压力超声心动图进行自动分析，并向临床医生提供自动分类。