Comparative modeling of mixed cardiopulmonary sounds in a low-resource paired dataset: Discrimination, calibration, and operating-point behavior - Runchen Cai (PLoS ONE, 2026)
Background Mixed cardiopulmonary recordings are common in bedside auscultation, yet most automated systems have been developed for isolated heart sounds or isolated respiratory sounds. Methods We conducted a comparative methods study on HLS-CMDS, a low-resource paired dataset containing mixed recordings with matched isolated heart and lung source recordings. The task was dual binary classification from a single mixed recording. We compared feature-based references, a shared-backbone multitask CNN, a target-domain student model, teacher-guided variants pretrained on PhysioNet/CinC 2016 and ICBHI 2017, and lighter source-aware variants using paired HLS-CMDS source recordings. A nested grouped five-fold evaluation was performed at the triplet level; within each outer training fold, an inner validation split was used for checkpoint selection, temperature scaling, and task-specific threshold selection. Results Under the revised nested evaluation, the light source-aware model showed the strongest mean discrimination (macro AUROC 0.7107 ± 0.1659; macro AUPRC 0.9318 ± 0.0423). The prevalence-defined no-skill macro AUPRC baseline was 0.8586 ± 0.0225. After inner-validation temperature scaling and threshold selection, the calibrated student-only model achieved the highest mean macro balanced accuracy (0.6894 ± 0.0548). The observed differences were interpreted cautiously because fold-to-fold variability was substantial. Conclusions In this small paired mixed-sound setting, restrained source-aware guidance showed the strongest discrimination tendency, whereas a simpler target-domain model achieved the best threshold-dependent balanced accuracy after calibration.