Key result
DL echocardiographic framework achieves up to ~97% accuracy for diagnosing pericardial disease.
Why the study?
TTE interpretation of pericardial disease is limited by operator dependence and incomplete functional assessment, and existing DL models focus mainly on effusion detection rather than broader evaluation.
Can a deep learning-based multi-view echocardiographic framework accurately diagnose pericardial disease morphology and haemodynamic significance?
Can a deep learning-based multi-view echocardiographic framework accurately diagnose pericardial disease morphology and haemodynamic significance?
A novel deep learning framework can accurately assess pericardial effusion size, thickening, and haemodynamic significance from multi-view echocardiograms, offering a comprehensive automated diagnostic tool.
Supports automated pericardial assessment in echo labs now; extends DL frameworks to multi-view morphological and haemodynamic evaluation.
Aims Pericardial disease spans a wide spectrum from small effusions to life-threatening tamponade or constriction. Transthoracic echocardiography (TTE) is the main diagnostic tool, but its interpretation is limited by operator dependence and incomplete functional assessment. Existing deep learning (DL) models focus mainly on effusion detection, lacking broader evaluation. Methods and results We developed a DL-based framework that performs sequential assessment of pericardial disease: (i) morphological features, including effusion amount (normal/small/moderate/large) and pericardial thickening/adhesion (yes/no), from five B-mode views, and (ii) haemodynamic significance (yes/no), incorporating Doppler and inferior vena cava measurements. The developmental dataset comprises 2253 TTEs from multiple Korean institutions (225 for internal testing), and the independent external test set consists of 274 TTEs. In the internal test set, diagnostic accuracy was 81.8–97.3% for effusion, 91.6% for thickening/adhesion, and 86.2% for haemodynamic significance. External test set accuracy was 80.3–94.2%, 94.5%, and 85.5%, respectively. Area under the receiver operating curves for the three tasks were 0.92–0.99, 0.90, and 0.79 internally, and 0.95–0.98, 0.85, and 0.76 externally. Sensitivity for thickening/adhesion and haemodynamic significance improved from 66.7% to 77.3%, and 68.8% to 80.8%, respectively, when poor image quality were excluded. Similar performance gains were observed in subgroups with complete target views and a higher number of available video clips. Conclusion This study presents the first DL-based TTE model for broader pericardial disease evaluation, integrating morphological with supportive functional assessments. The proposed framework demonstrated strong generalizability and aligned with the real-world diagnostic workflow. However, caution is warranted when interpreting results under suboptimal imaging conditions.
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Jeong et al. (2026) studied this question. The DL echocardiographic framework achieved 80.3–97.3% accuracy and AUCs up to 0.99 for morphological and functional diagnosis of pericardial disease.
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