Deep Learning for Echo Analysis, Tracking, and Evaluation of Mitral Regurgitation (DELINEATE-MR)

作者：Aaron Long, Christopher M. Haggerty, J. Finer, Dustin N. Hartzel, Linyuan Jing, Azadeh Keivani, Christopher Kelsey, Daniel Rocha, J. Ruhl, D. vanMaanen, G. Metser, E. Duffy, T. Mawson, Mathew S. Maurer, A. Einstein, A. Beecy, D. Kumaraiah, S. Homma, Qi Liu, Vratika Agarwal, Mark A. Lebehn, Martin Leon, Rebecca T. Hahn, P. Elias, T. Poterucha · 发表于：Circulation · 年份：2024 · DOI：10.1161/circulationaha.124.068996 · 被引用次数：42 · 研究领域：Medicine

BACKGROUND: Artificial intelligence, particularly deep learning (DL), has immense potential to improve the interpretation of transthoracic echocardiography (TTE). Mitral regurgitation (MR) is the most common valvular heart disease and presents unique challenges for DL, including the integration of multiple video-level assessments into a final study-level classification. METHODS: A novel DL system was developed to intake complete TTEs, identify color MR Doppler videos, and determine MR severity on a 4-step ordinal scale (none/trace, mild, moderate, and severe) using the reading cardiologist as a reference standard. This DL system was tested in internal and external test sets with performance assessed by agreement with the reading cardiologist, weighted κ, and area under the receiver-operating characteristic curve for binary classification of both moderate or greater and severe MR. In addition to the primary 4-step model, a 6-step MR assessment model was studied with the addition of the intermediate MR classes of mild-moderate and moderate-severe with performance assessed by both exact agreement and ±1 step agreement with the clinical MR interpretation. RESULTS: A total of 61 689 TTEs were split into train (n=43 811), validation (n=8891), and internal test (n=8987) sets with an additional external test set of 8208 TTEs. The model had high performance in MR classification in internal (exact accuracy, 82%; κ=0.84; area under the receiver-operating characteristic curve, 0.98 for m...