Abstract 15206: Stethaid: Automated Point-of-care Recognition of Innocent Still’s Murmur in Children

Abstract

Introduction: Still’s murmurs are difficult to distinguish from other heart murmurs at the primary care level. This leads to an estimated 400,000 children being referred to pediatric cardiologists each year for evaluation of heart murmurs in the United States. The murmur is ultimately diagnosed as innocent Still’s murmur in approximately 90% of these children. This diagnosis requires no specialty referral, cardiac testing, exercise restrictions or cardiology follow-up. These unnecessary referrals and associated tests waste healthcare resources, add to healthcare costs, and are a source of avoidable anxiety in children and families while waiting to see a pediatric cardiologist. Hypothesis: We have developed a novel mobile technology to discriminate Still’s murmur from all other murmurs. A smartphone application (app) that could quickly distinguish Still’s murmur from all other murmurs with high accuracy at the point of care could reassure pediatricians in their identification of Still’s murmur, significantly reducing the current rate of unnecessary referrals to cardiologists. Methods: Our current prototype is StethAid, a smartphone app which accepts heart sound recordings, and a cloud-based deep learning algorithm for discriminating Still’s murmur from other murmurs. The algorithm was independently developed and evaluated using archived heart sounds, recorded by one of the authors (RWD), made using an electronic stethoscope (3M Littmann Model 4100). Results: Our algorithm offers a sensitivity (Still’s murmurs correctly identified) of 90% with a specificity (Non-Still’s murmurs correctly identified) of 99% on 5-fold cross-validation over the Murmur Library. The area under the curve was 0.99446. The algorithm’s result is available in real time at the point-of-care (&lt 1 min). Conclusions: The described point-of-care mobile technology could automatically distinguish Still’s murmur from all other murmurs with high accuracy. The technology could lower the current high rate of specialist referrals associated with Still’s murmur and reduce the related financial and emotional costs. Future directions will include further improvement of the technology and validation through multi-center clinical trials.