Automated evaluation for pericardial effusion and cardiac tamponade with echocardiographic artificial intelligence
Year: 2025
Type: article
Abstract: Aims: Timely and accurate detection of pericardial effusion and assessment of cardiac tamponade remain challenging and highly operator dependent. Objectives: Artificial intelligence has advanced many echocardiographic assessments, and we aimed to develop and validate a deep learning model to automate the assessment of pericardial effusion severity and cardiac tamponade from echocardiogram videos. Methods and results: We developed a deep learning model (EchoNet-Pericardium) using temporal-spatial convolutional neural networks to automate pericardial effusion severity grading and tamponade detection from echocardiography videos. The model was trained using a retrospective dataset of 1 427 660 videos from 85 380 echocardiograms at Cedars-Sinai Medical Centre (CSMC) to predict PE severity and cardiac tamponade across individual echocardiographic views and an ensemble approach combining predictions from five standard views. External validation was performed on 33 310 videos from 1806 echocardiograms from Stanford Healthcare (SHC). In the held-out CSMC test set, EchoNet-Pericardium achieved an AUC of 0.900 (95% CI: 0.884-0.916) for detecting moderate or larger pericardial effusion, 0.942 (95% CI: 0.917-0.964) for large pericardial effusion, and 0.955 (95% CI: 0.939-0.968) for cardiac tamponade. In the SHC external validation cohort, the model achieved AUCs of 0.869 (95% CI: 0.794-0.933) for moderate or larger pericardial effusion, 0.959 (95% CI: 0.945-0.972) for large pericardial effusion, and 0.966 (95% CI: 0.906-0.995) for cardiac tamponade. Subgroup analysis demonstrated consistent performance across ages, sexes, left ventricular ejection fraction, and atrial fibrillation statuses. Conclusion: Our deep learning-based framework accurately grades pericardial effusion severity and detects cardiac tamponade from echocardiograms, demonstrating consistent performance and generalizability across different cohorts. This automated tool has the potential to enhance clinical decision-making by reducing operator dependence and expediting diagnosis.
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,725
Awards: Utilization of Artificial Neural Network and Transfer Learning to Predict Dyskalemia Based on Electrocardiogram in Patients Receiving Regular Hemodialysis., Artificial Intelligence Imaging Biomarkers of Longitudinal Cardiovascular Stress, Deep Learning Based Phenotyping and Outcomes Prediction for Valvular Heart Disease, Deep Learning Assessment of the Right Ventricle: Function, Etiology, and Prognosis
OpenAlex ID: W4414431587