Valvular & structural heart diseaseDiagnostic or prognostic study
Deep learning for echocardiographic assessment and risk stratification of aortic, mitral, and tricuspid regurgitation: the DELINEATE-regurgitation study
- Diagnostic accuracy and prognostic model development study
- two sites
- internal and external test sets
- deep learning
Authors' conclusion
An AI system can accurately classify AR, MR, and TR and predict MR progression beyond currently known risk factors.
Study context
- Population
- Transthoracic echocardiograms with colour Doppler videos assessed for aortic, mitral and tricuspid regurgitation
- Participants
- 71,660 TTEs
- Comparison
- DELINEATE-Regurgitation artificial intelligence (AI) vs cardiologist interpretation
- Outcome
- Agreement of AI regurgitation severity classification with cardiologists (weighted kappa)
Performance
As reported in the abstract
Weighted kappa, multiview approach
| Test or model | Aortic (AR) | Mitral (MR) | Tricuspid regurgitation (TR) |
|---|---|---|---|
| Internal test set | 0.81 | 0.76 | 0.73 |
| External test set | 0.76 | 0.72 | 0.64 |
Further findings
MR progression to moderate-severe or severe MR (DELINEATE-MR-Progression AI score), adjusted for clinical factors
| Comparison | HR (95% CI) | |
|---|---|---|
| AI score · internal test set | 4.1 (2.5–6.6) | |
| 0.10.31310Hazard ratio (log scale) |
Original publication
Deep learning for echocardiographic assessment and risk stratification of aortic, mitral, and tricuspid regurgitation: the DELINEATE-regurgitation study. Eur Heart J. Published 29 March 2025. doi:10.1093/eurheartj/ehaf248
Summarised from the published abstract; every number is checked against it