Key result
Deep learning automated LVEF shows highly variable AUC for heart failure diagnosis across different populations.
Why the study?
Concerns challenge the clinical translation of automated measurements in cardiac imaging using deep learning from research to practice.
Incorporating imaging domain knowledge and carefully considering evaluation metrics and training data distribution can significantly improve the robustness and generalizability of deep learning models for automated cardiac measurements.
Population-dependent performance variability cautions against broad clinical deployment of automated LVEF DL; leaves open optimal strategies for robust generalizability.
BACKGROUND: Automated measurements in cardiac imaging with the use of deep learning (DL) is a highly active area of research and innovation. However, some concerns challenge the translation of DL methods from research to clinical implementation. OBJECTIVES: The authors evaluated 3 challenges for cardiac measurements by DL using left ventricular ejection fraction (LVEF) for management of heart failure and discuss mitigation strategies. METHODS: Using 3 different populations (N = 3,538), automated LVEF measurements were obtained with the use of supervised end-to-end learning and analyzed in terms of HF management. Three common challenges related to evaluation metrics, training data, and model generalization were studied. RESULTS: For the evaluation challenge, the authors identified significant unreliability of the AUC when applied to dichotomized heart failure diagnosis. Specifically, AUC varied from 0.71 to 0.98 owing solely to changes in population characteristics. For the training data challenge, model performance could be enhanced even after reducing the number of training subjects by 40%. For the generalization challenge, a performance degradation was observed compared with internal data when testing the model on external data. Integrating medical imaging domain knowledge in the DL framework effectively helped to recover performance and improve generalizability. CONCLUSIONS: Both training data and generalization aspects challenge the performance of DL algorithms for automated cardiac measurements. In addition, evaluation metrics challenge the ability to detect underperforming algorithms. By considering evaluation metrics and training data distribution, and incorporating imaging domain knowledge, the design and evaluation of DL models can be improved, leading to more robust models, improved interpretation, and easier comparison across data sets. These findings may guide researchers and clinicians in implementing DL models for cardiovascular imaging.
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Pasdeloup et al. (2025) studied Heart failure (n=3,538). Deep learning for automated LVEF measurements was evaluated on AUC for dichotomized heart failure diagnosis. Automated LVEF measurement using deep learning demonstrated significant AUC variability (0.71 to 0.98) for heart failure diagnosis based solely on population characteristics.
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