Population-Based Feasibility of AI-Enabled Self-Auscultation using Smartphones: Findings from 109,882 Recordings Across Three Countries

Traditional auscultation remains diagnostically valuable but is limited by examiner subjectivity and access. Smartphone-based AI phonoscopy offers an opportunity for scalable self-screening for valvular heart disease. Stethophone®, an AI-enabled software as a medical device, was made available for download in Canada, the United States, and Ukraine. Users recorded heart sounds through the smartphone’s built-in microphones, processed and enhanced in real time using patented audio algorithms. Quality was automatically assessed, and users were prompted to re-record as needed. Recordings were analyzed for murmurs and aortic stenosis signatures using AI phonoscopy. Among 28,188 users producing 109,882 recordings, 91.7% of lay users and 89.0% of healthcare professionals achieved a clinically interpretable recording on the first attempt. Lay-users and healthcare professionals performed similarly (91% vs 89%). The most successful auscultation site was the tricuspid point; the left carotid least. Cardiac murmurs were detected in 16.3% of recordings, corresponding to 15.6% of users. Structural murmurs were identified in 7.0% of users (4.9% of recordings). Murmurs consistent with aortic stenosis were present in 6.3% of users. This study demonstrates that self-auscultation with a smartphone and dedicated medical software is highly feasible in lay populations, with quality results comparable to clinicians. Pathological murmurs, notably those of aortic stenosis, are common and highlight the potential for population-wide early detection of cardiac disorders.