Interpretable Deep Learning for Opportunistic Screening of Left Ventricular Dysfunction Screening Using Chest Radiographs.

Purpose To develop and evaluate a deep learning model (DLLV) for identifying reduced left ventricular ejection fraction (LVEF) on chest radiographs (CXRs) and assess its potential as a prescreening tool for selective transthoracic echocardiography (TTE) in health checkup populations. Materials and Methods Same-day CXR-TTE pairs obtained between January 2016 and September 2021 were retrospectively collected for model development. Counterfactual learning was applied to enhance model performance and interpretability. External testing included one tertiary referral center cohort and two health checkup cohorts. Discrimination was evaluated using area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPRC), and DLLV-based prescreening was evaluated by estimated TTE reduction and missed-case burden. Results The development dataset included 24,674 patients (32,135 CXR-TTE pairs); external cohorts included 5440, 28,034, and 6085 patients (53-62% male). DLLV achieved an AUC of 0.86 (95% CI: 0.84, 0.88) for LVEF ≤ 40% in the tertiary referral cohort. In health checkup cohorts, AUCs were 0.92-0.97 for LVEF ≤ 40%, with AUPRCs of 0.04 in both cohorts, corresponding to 87-fold (95% CI: 16, 404) and 37-fold (95% CI: 3, 251) enrichment over baseline prevalence. Counterfactual reinforcement learning produced heatmaps with greater heart localization precision (P < .001 for all) and higher AUC for LVEF < 50% (P = .04). DLLV-based prescreening estimated TTE volume reductions up to 85% while identifying all 12 and 7 individuals with LVEF ≤ 40%. Conclusion DLLV identified reduced LVEF on CXRs across external test cohorts and showed potential as a prescreening tool for selective TTE in health checkup populations. ©RSNA, 2026.