Heart murmur detection from phonocardiogram recordings: The George B. Moody PhysioNet Challenge 2022
Authors: Reyna Matthew A. , Kiarashi Yashar , Elola Andoni , Oliveira Jorge , Renna Francesco , Gu Annie , Perez Alday Erick A. , Sadr Nadi , Sharma Ashish , Kpodonu Jacques , Mattos Sandra , Coimbra Miguel T. , Sameni Reza , Rad Ali Bahrami , Clifford Gari D. 
Published: August 2022

Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs for follow-up diagnostic screening and treatment for abnormal cardiac function. However, experts are needed to interpret the heart sounds, limiting the accessibility of auscultation for cardiac care in resource-constrained environments. Therefore, the George B. Moody PhysioNet Challenge 2022 invited teams to develop algorithmic approaches for detecting heart murmurs and abnormal cardiac function from phonocardiogram (PCG) recordings of the heart sounds.

For the Challenge, we sourced 5272 PCG recordings from 1568 pediatric patients in rural Brazil, and we invited teams to implement diagnostic screening algorithms for detecting heart murmurs and abnormal cardiac function from the recordings. We required the participants to submit the complete code for training and running their algorithms, improving the transparency, reproducibility, and utility of their work. We also devised an evaluation metric that considered the costs of screening, diagnosis, treatment, and diagnostic errors, allowing us to investigate the benefits of algorithmic diagnostic screening and facilitate the development of more clinically relevant algorithms.

We received 779 algorithms from 87 teams during the course of the Challenge, resulting in 53 working codebases for detecting heart murmurs and abnormal cardiac function from PCGs. These algorithms represent a diversity of approaches from both academia and industry.

The use of heart sound recordings for identifying heart murmurs and abnormal cardiac function allowed us to explore the potential of algorithmic approaches for providing accessible pre-screening in resource-constrained environments. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and clinical relevance of the research from the Challenge.

Author summary

Cardiac auscultation is an accessible diagnostic screening tool for identifying heart murmurs. However, experts are needed to interpret heart sounds, limiting the accessibility of auscultation in cardiac care. The George B. Moody PhysioNet Challenge 2022 invited teams to develop algorithms for detecting heart murmurs and abnormal cardiac function from phonocardiogram (PCG) recordings of heart sounds.

For the Challenge, we sourced 5272 PCG recordings from 1568 pediatric patients in rural Brazil. We required the participants to submit the complete code for training and running their algorithms, improving the transparency, reproducibility, and utility of their work. We also devised an evaluation metric that considered the costs of screening, diagnosis, treatment, and diagnostic errors, allowing us to investigate the benefits of algorithmic diagnostic screening and facilitate the development of more clinically relevant algorithms. We received 779 algorithms from 87 teams during the Challenge, resulting in 53 working codebases and publications that represented a diversity of approaches to detecting heart murmurs and identifying clinical outcomes from heart sound recordings.