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

Abstract

Objective: Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs for follow-up diagnostic screening and treatment, especially in resource-constrained environments. However, experts are needed to interpret the heart sound recordings, limiting the accessibility of auscultation for cardiac care. The George B. Moody PhysioNet Challenge 2022 invites teams to develop automated approaches for detecting abnormal heart function from multi-location phonocardiogram (PCG) recordings of heart sounds. Approach: For the Challenge, we sourced 5272 PCG recordings from 1568 pediatric patients in rural Brazil. We required the Challenge participants to submit the complete code for training and running their models, improving the transparency, reproducibility, and utility of the diagnostic algorithms. We devised a cost-based evaluation metric that captures the costs of screening, treatment, and diagnostic errors, allowing us to investigate the benefits of algorithmic pre-screening and facilitate the development of more clinically relevant algorithms. Main results: So far, over 80 teams have submitted over 600 algorithms during the course of the Challenge, representing a diversity of approaches in academia and industry. We will update this manuscript to share an analysis of the Challenge after the end of the Challenge. Significance: The use of heart sound recordings for both heart murmur detection and clinical outcome identification allowed us to explore the potential of automated approaches to provide accessible pre-screening of less-resourced populations. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and relevance of the researched conducted during the Challenge.

Multiple Instance Learning Framework can Facilitate Explainability in Murmur Detection

“…where L CE is the cross-entropy loss, y m , y o are the murmur / outcome labels and ỹm , ỹo the murmur / outcome prediction respectively. The weights are adjusted based on the factors in the weighted accuracy metric [2], where ŵo = w o [1, 5] T (Normal, Abnormal) and ŵm = w m [1, 3, 5] T (Absent, Unknown, Present).…”

“…We use AUROC, AUPRC, F1, accuracy and the metrics defined by [2], which are weighted accuracy and outcome cost, to validate our model. The weighted accuracy is defined as…”

“…where m PP , m UU , m AA are the number of correctly predicted labels "Present", "Unknown", "Absent" respectively. m P U is the number of examples predicted as "Unknown" but labeled "Present" and so forth [2].…”

“…The functions are each dependent on the number of true positive n TP , false positive n FP , false negative n FN , true negative n TN predictions, and the total number of patients n patients . Additional information can be found in [2].…”

“…Heart murmurs are audible vibrations caused by perturbations of the blood flow such as strong pressure gradients or velocity changes and are often indicators for CVDs. For this reason, the most recent George B. Moody PhysioNet challenge 2022 [2,3] raised the task of detecting heart murmurs in PCG recordings from four different auscultation locations of a pediatric population and predicting the clinical outcome of a full diagnostic screening. For children, this is particularly interesting because 80 % of children have heart murmurs at the early stage of their life [4].…”