Key result
Deep learning model accurately classifies mitral valve etiologies from limited echocardiograms with ~0.97 to 0.99 AUROC.
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Why the study?
Accurate etiologic classification of the mitral valve guides clinical management but relies on expert visual interpretation, with automated morphologic interpretation under routine imaging conditions remaining limited.
Does a multi-view deep learning framework using limited routine echocardiographic views accurately classify mitral valve disease etiologies compared with expert visual assessment?
Observational (n=6,606)
Yes
Does a multi-view deep learning framework using limited routine echocardiographic views accurately classify mitral valve disease etiologies compared with expert visual assessment?
A deep learning model using limited standard echocardiographic views can accurately and reliably classify major mitral valve etiologies across varied image qualities and external validation cohorts, facilitating scalable automated evaluation.
May aid etiology classification from routine views; extends AI echocardiography analysis but remains hypothesis-generating pending prospective trials.
BACKGROUND: Accurate etiologic classification of the mitral valve (MV) is essential for guiding clinical management but remains dependent on expert visual interpretation. Despite advances in artificial intelligence (AI)-based quantitative analysis, automated morphologic interpretation under routine imaging conditions remains limited. OBJECTIVES: To develop and validate a deep learning (DL) framework for multiclass classification for major MV etiologies using limited routine transthoracic echocardiography (TTE) views. METHODS: A multi-view DL model was developed to classify five MV etiologies (normal, rheumatic, degenerative, prolapse, and functional). The developmental dataset comprised 4,344 TTE examinations from a nationwide multicenter registry. Validation was performed using an internal test dataset and an independent external test dataset (2,262 TTE examinations). Prespecified subgroup analyses were conducted according to mitral regurgitation (MR) severity and automated image quality (IQ). RESULTS: The model demonstrated robust performance across all MV etiologies in both internal and external datasets. In the internal test dataset, area under the receiver operating characteristic curve (AUROC) values ranged from 0.968 to 0.997 across etiologies, with higher performance observed for normal valves and rheumatic disease. In the external test dataset, discriminative performance remained preserved (AUROC, 0.931-0.992), despite differences in disease distribution and MR severity. Sensitivity for MV prolapse increased markedly with moderate or greater MR compared with mild MR, whereas degenerative disease showed persistently lower sensitivity across MR severity. Diagnostic performance remained stable across IQ strata, with comparable accuracy and macro-F1 scores in all-adequate and partially suboptimal examinations. In post-hoc analyses of cases with multiple MV etiologies, the model correctly identified at least one expert-assigned etiology in 85.7% of cases. CONCLUSIONS: DL-based analysis of limited, routinely acquired TTE views enables reliable multiclass classification of MV etiologies. This approach may complement quantitative automation and expert visual assessment, supporting more consistent and scalable MV evaluation in routine echocardiographic practice.
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Jeong et al. (2026) conducted an observational in Mitral Valve Disease (n=6,606). Deep learning-based multi-view echocardiography classification model vs. Expert visual interpretation (ground truth) was evaluated on Discriminative diagnostic performance (AUROC) for classifying 5 mitral valve etiologies on an internal test dataset. The deep learning model accurately classified mitral valve etiologies using limited echocardiography views, achieving AUROC values ranging from 0.968 to 0.997 in the internal test dataset and 0.931 to 0.992 in the external test dataset.
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