Automated Analysis of Heart Sound Signals in Screening for Structural Heart Disease in Children

Abstract

Purpose Our aim was to investigate the ability of an artificial intelligence (AI)-based algorithm to differentiate innocent murmurs from pathologic ones. Methods An AI-based algorithm was developed using heart sound recordings collected from 1413 patients at the five university hospitals in Finland. The corresponding heart condition was verified using echocardiography. In the second phase of the study, patients referred to Helsinki New Children’s Hospital due to a heart murmur were prospectively assessed with the algorithm, and then the results were compared with echocardiography findings. Results Ninety-eight children were included in this prospective study. The algorithm classified 72 (73%) of the heart sounds as normal and 26 (27%) as abnormal. Echocardiography was normal in 63 (64%) children and abnormal in 35 (36%). The algorithm recognised abnormal heart sounds in 24 of 35 children with abnormal echocardiography and normal heart sounds with normal echocardiography in 61 of 63 children. When the murmur was audible, the sensitivity and specificity of the algorithm were 83% (24/29) (confidence interval (CI) 64–94%) and 97% (59/61) (CI 89–100%), respectively. Conclusions The algorithm was able to distinguish murmurs associated with structural cardiac anomalies from innocent murmurs with good sensitivity and specificity. The algorithm was unable to identify heart defects that did not cause a murmur. Further research is needed on the use of the algorithm in screening for heart murmurs in primary health care.