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Multicenter Study
. 2025 Jul 21;46(28):2780-2791.
doi: 10.1093/eurheartj/ehaf248.

Deep learning for echocardiographic assessment and risk stratification of aortic, mitral, and tricuspid regurgitation: the DELINEATE-regurgitation study

Aaron Long  1   2   3 , Joshua Finer  4 , Heidi Hartman  1   2 , Dustin Hartzel  4 , Linyuan Jing  4 , Christopher Kelsey  4 , Daniel Rocha  4 , Jeffrey Ruhl  4 , David vanMaanen  4 , Youssef Elnabawi  1   2 , Gil Metser  1   2 , Eamon Duffy  1   2 , Thomas Mawson  1   2 , Emily Tat  1   2 , Christopher M Haggerty  3   4 , Mathew Maurer  1   2 , Andrew J Einstein  1   2   5 , Ashley N Beecy  4   6 , Deepa Kumaraiah  1   2   6 , Richard B Devereux  6 , Shunichi Homma  1   2 , Martin Leon  1   2   7 , Rebecca T Hahn  1   2 , Pierre Elias  1   2   3   4 , Timothy J Poterucha  1   2   8   9
Affiliations
Multicenter Study

Deep learning for echocardiographic assessment and risk stratification of aortic, mitral, and tricuspid regurgitation: the DELINEATE-regurgitation study

Aaron Long et al. Eur Heart J. .

Abstract

Background and aims: Classification and risk stratification in aortic (AR), mitral (MR), and tricuspid regurgitation (TR) remains a significant clinical challenge. This study aimed to develop an artificial intelligence (AI) system to assess valvular regurgitation and stratify MR-progression risk.

Methods: Using transthoracic echocardiograms (TTEs) at two sites (internal development/test, external test), the DELINEATE-Regurgitation system was developed to classify AR, MR, and TR severity using colour Doppler videos. Methods of summating video-level classifications into study-level predictions were tested, comparing single-view with multiview approaches integrating predictions across multiple videos. Model agreement with cardiologists was assessed by weighted kappa. A separate AI system (DELINEATE-MR-Progression) analysing colour Doppler videos was developed to predict which patients with mild, mild-moderate, and moderate MR were most likely to progress to moderate-severe or severe MR with analysis by Kaplan-Meier and Cox proportional hazards models.

Results: A total of 71 660 TTEs with 1 203 980 colour Doppler videos were included. The weighted kappa in internal/external test sets for regurgitation classification was 0.81/0.76 for AR, 0.76/0.72 for MR, and 0.73/0.64 for TR using a multiview approach taking all colour Doppler videos in a study, demonstrating substantial agreement with cardiologist interpretation with superiority of multiview over single view approaches. In the progression analysis, the AI score stratified MR-progression risk even when controlled for clinical factors known to be associated with MR progression [internal test set hazard ratio 4.1 (95% confidence interval 2.5-6.6)].

Conclusions: An AI system can accurately classify AR, MR, and TR and predict MR progression beyond currently known risk factors.

Keywords: Aortic regurgitation; Artificial intelligence; Deep learning; Echocardiography; Mitral regurgitation; Tricuspid regurgitation.

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Figures

Structured Graphical Abstract
Structured Graphical Abstract
Figure 1
Figure 1
Patient Flow for Regurgitation Classification Cohort (Panel A) and Mitral Regurgitation Progression Cohort (Panel B). These figures demonstrate patient flow for the AR, MR, and TR classification study (Panel A) and the MR progression study (Panel B)
Figure 2
Figure 2
Model Performance by Confusion Matrices for Multiview All Model for the 4-Grade Classification of Aortic Regurgitation (Panel A and B), Mitral Regurgitation (Panel C and D), and Tricuspid Regurgitation (Panel E and F). These figures demonstrate the agreement of the AI valvular regurgitation classification system to cardiologist assessment in the internal and external test sets for AR, MR, and TR
Figure 3
Figure 3
Kaplan–Meier Analysis of using the DELINEATE-MR-Progression Deep Learning Model to Discriminate Risk of MR Progression in the Internal Test Set (panel A) and External Test Sets (panel B). These figures demonstrate the use of the DELINEATE-MR-Progression model to predict the risk of progression in patients with mild, mild–moderate, and moderate MR. The AI risk was stratified by tertiles of risk for each grade of MR and accurately stratified risk of progression to moderate–severe or severe MR by 2 years
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References

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