Echocardiographic measures read by artificial intelligence enable accurate and rapid prediction of the worsening of heart failure
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Abstract
Aims
Automatic echocardiographic measurements using artificial intelligence have shown promising results; however, they have not been compared with manual measurements regarding heart failure (HF) progression and algorithm runtime.
Methods and results
Data came from the prospective HF study MyoVasc (NCT04064450), which involved a highly standardized 5-h examination, including comprehensive echocardiography, at a dedicated study centre between January 2013 and April 2018. Worsening of HF was a primary composite endpoint, recorded by structured follow-up, death certificates, and medical records. The automated assessment was performed using EchoDL, eight 3D convolutional neural networks (CNNs) trained to predict clinical parameters. Manual and automatic left ventricular ejection fraction (LVEF), E/E′-ratio and left ventricular mass (LVM) demonstrated a good intraclass correlation coefficient {LVEF: 0.75 [95% confidence interval (CI) 0.75–0.77], E/E′-ratio: 0.59 [CI 0.56–0.61], LVM: 0.64 [CI 0.62–0.66]}. After a median follow-up of 3.8 years (IQR 2.1–5.0), 470 patients experienced worsening of HF. In multivariable Cox analysis, comparison of manually and automatically assessed LVEF, E/E′-ratio and LVM demonstrated risk estimates slightly in favour of the CNNs. Direct comparison of C-indices showed significantly better model performance for automatically determined LVEF (0.71 vs. 0.73, P = 0.038) and E/E′-ratio (0.64 vs. 0.66, P = 0.013) and a trend for LVM (0.66 vs. 0.68, P = 0.063). Echo-DL required an average of 1053.4 ms (95% CI 1050.7–1056.0) to analyse a four-second-long echocardiogram.
Conclusion
Automated analysis of echocardiograms using 3D CNNs was comparable to manual measurements in predicting HF-specific outcomes. Echo-DL offers potential time savings and improved risk prediction in clinical settings, allowing integration into echocardiographic hardware.
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European heart journal - digital health, 6, Oxford University Press, Oxford, 2025, https://doi.org/10.1093/ehjdh/ztaf120