An AI classification tool for stress echocardiograms increased clinicians' sensitivity for detecting severe coronary artery disease by 10%, achieving an area under the ROC curve of 0.93.
RCT
Crossover
Yes
Does an AI classification tool improve the diagnostic accuracy of stress echocardiograms for detecting severe coronary artery disease?
An AI classification tool for stress echocardiography can accurately identify severe coronary artery disease and significantly improve clinician sensitivity and confidence.
Effect estimate: AUC 0.93
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.
Upton et al. (Wed,) conducted a rct in Severe coronary artery disease. Artificially intelligent (AI) classification tool vs. Clinical interpretation without AI was evaluated on Detection of severe coronary artery disease (AUC 0.93). An AI classification tool for stress echocardiograms increased clinicians' sensitivity for detecting severe coronary artery disease by 10%, achieving an area under the ROC curve of 0.93.
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