Heart murmur detection from phonocardiogram recordings: The George B. Moody PhysioNet Challenge 2022

• Type: Another Publication in an International Scientific Journal
• Date: 2023

Authors

Reyna, A (Author) Other; Kiarashi, Y (Author) Other; Elola, A (Author) Other; Oliveira, J (Author) Other; Renna, F (Author) FCUP; Gu, A (Author) Other; Perez Alday, A (Author) Other; Sadr, N (Author) Other; Sharma, A (Author) Other; Kpodonu, J (Author) Other; Mattos, S (Author) Other; Coimbra, M (Author) FCUP; Sameni, R (Author) Other; Rad, AB (Author) Other; Clifford, D (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: PLOS Digital Health

Vol. 2

ISSN: 2767-3170

Publisher: Public Library of Science

Indexing

Scopus - 12 Citations

Other information

Authenticus ID: P-010-T3M

DOI: 10.1371/journal.pdig.0000324

Abstract (EN): Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and treatment for abnormal cardiac function. However, experts are needed to interpret the heart sounds, limiting the accessibility of cardiac auscultation 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 heart sounds. For the Challenge, we sourced 5272 PCG recordings from 1452 primarily 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 training and inference code for their algorithms, improving the transparency, reproducibility, and utility of their work. We also devised an evaluation metric that considered the costs of screening, diagnosis, misdiagnosis, and treatment, 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 for detecting heart murmurs and abnormal cardiac function from PCG recordings. These algorithms represent a diversity of approaches from both academia and industry, including methods that use more traditional machine learning techniques with engineered clinical and statistical features as well as methods that rely primarily on deep learning models to discover informative features. 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 more accessible diagnostic screening in resourceconstrained 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. © 2023 Reyna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Language: English

Type (Professor's evaluation): Scientific