Deep Learning-Based Multi-View Echocardiographic Framework for Comprehensive Diagnosis of Pericardial Disease

Background 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.

We developed a DL-based framework that performs sequential assessment of pericardial disease: (1) morphological features, including effusion amount (normal/small/moderate/large) and pericardial thickening/adhesion (yes/no), from five B-mode views, and (2) hemodynamic significance (yes/no), incorporating Doppler and inferior vena cava measurements. The developmental dataset comprises 2,253 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 hemodynamic significance. External test set accuracy was 80.3-94.2%, 94.5%, and 85.5%, respectively. Area under the receiver operating curves (AUROCs) for the three tasks was 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 hemodynamic 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.

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. Competing Interest Statement J.J., J.K., S.A.L., Y.J., and Y.E.Y. are currently affiliated with Ontact Health, Inc. Y.E.Y., and H.J.C. holds stock in Ontact Health, Inc. The other authors have no conflicts of interest to declare. Funding Statement This work was supported by a grant from the Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Korea government (Ministry of Science and ICT) (No.2022000972, Development of a Flexible Mobile Healthcare Software Platform Using 5G MEC); and the Medical AI Clinic Program through the NIPA funded by the MSIT. (Grant No.: H0904-24-1002). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Institutional Review Board of Yonsei University Health System, Seoul National University Hospital, Chungnam National University Hospital, Hanyang University Hospital, and Soonchunhyang University Bucheon Hospital gave ethical approval for this work and waived the requirement for informed consent (protocol Nos. 2021-0147-003, CNUH 2021-04-032, HYUH 2021-03-026-003, SCHBC 2021-03-007-001, B 2104/677-004). In addition, Institutional Review Board of Uijeongbu Eulji University Hospital gave ethical approval for the external test dataset (protocol No. 2025-03-018). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes This version includes clarifications of the study scope and revisions for improved clarity and readability across the manuscript. No changes were made to the scientific content or conclusions. Data Availability The AI-Hub dataset used in this study may be available upon proper request and approval of a formal proposal. The external test dataset cannot be made publicly shared due to ethical restrictions imposed by the IRB of the study institution, as public access could compromise patient confidentiality and privacy. Researchers interested in accessing the minimal anonymized dataset may contact the corresponding author (yeonyeeyoon{at}gmail.com) for further information.