Modelling the Interplay between Responsive Individual Vaccination Decisions and the Spread of SARS-CoV-2

• Type of Material: Article
• Authors: Matthew A. Reyna, Emory University; Yash Kiarashi, Emory University; Andoni Elola, University of the Basque Country UPV/EHU; Jorge Oliveira, Universidade Portucalense; Francesco Renna, Universidade do Proto; ANNIE (PING) GU, Emory University; Erick A. Perez Alday, Emory University; Nadi Sadr, Emory University; Ashish Sharma, Emory University; Jacques Kpodonu, Harvard Medical School; Sandra Mattos, Real Hospital Português; Miguel T. Coimbra, Universidade do Porto; Reza Sameni, Emory University; Ali Bahrami Rad, Emory University; Gari D. Clifford, Emory University
• Language: English
• Date: 2023-09-11
• Publisher: PLOS
• Publication Version: Final Published Version
• Copyright Statement: © 2023 Reyna et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): https://doi.org/10.1371%2Fjournal.pdig.0000324
• Title of Journal or Parent Work: PLOS Digital Health
• Volume: 2
• Issue: 9
• Start Page: e0000324
• Grant/Funding Information: This research is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant numbers 2R01GM104987-09 and R01EB030362 respectively, the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378, as well as the Gordon and Betty Moore Foundation and MathWorks under unrestricted gifts. AE receives financial support through grant PID2021-122727OB-I00 funded by MCIN/AEI/10.13039/501100011033 and "ERDF A way of making Europe" and by the Basque Government under Grant IT1717-22. FR and MC receive financial support by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020.
• Keywords: heart murmur; cardiology; phonocardiogram recordings
• Research Categories: Health Sciences, Public Health

Abstract

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 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.