Stethoscope, meet AI – hearing hidden sounds to better diagnose disease (theconversation.com)

Researchers are combining century‑old auscultation with modern AI to detect heart disease earlier and more accurately than the human ear. Using digital stethoscopes to record cardiac sounds, teams label those recordings and train supervised algorithms to spot subtle spectral and temporal differences between healthy and diseased hearts that are imperceptible to clinicians. To address the scarcity of very early‑stage human data, the group also used animal models to expose the model to early pathological signatures, then validated its predictions against imaging of cardiac calcium buildup. The work matters because audible signs like murmurs often appear late or overlap across conditions, limiting traditional diagnosis. The AI approach reportedly classifies healthy heart sounds with >95% accuracy and differentiates disease types at ~85%, and—crucially—detects disease before murmurs or structural changes are obvious. If externally validated in larger human cohorts, this low‑cost, noninvasive pipeline could augment bedside screening, triage, and timely intervention. Remaining challenges include dataset bias toward moderate‑to‑severe cases, translating animal‑derived signals to humans, and broader clinical validation, but the approach signals a practical path to make stethoscopes smarter and earlier disease detection more accessible.