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
Department of Biomedical Informatics, Emory University, Atlanta, Georgia, United States of America.
Department of Electronic Technology, University of the Basque Country UPV/EHU, Eibar, Spain.
PLOS Digit Health. 2023 Sep 11;2(9):e0000324. doi: 10.1371/journal.pdig.0000324. eCollection 2023 Sep.
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.
心脏听诊是一种易于使用的诊断筛查工具，可帮助识别有心脏杂音的患者，这些患者可能需要针对心脏功能异常进行后续诊断筛查和治疗。然而，需要专家来解读心音，这限制了心脏听诊在资源有限环境中的可及性。因此，2022年乔治·B·穆迪生理信号挑战赛邀请各团队开发算法方法，用于从心音的心音图（PCG）记录中检测心脏杂音和心脏功能异常。对于此次挑战赛，我们从巴西农村地区的1452名主要为儿科患者中获取了5272份PCG记录，并邀请各团队从这些记录中实施用于检测心脏杂音和心脏功能异常的诊断筛查算法。我们要求参与者提交其算法的完整训练和推理代码，以提高其工作的透明度、可重复性和实用性。我们还设计了一种评估指标，该指标考虑了筛查、诊断、误诊和治疗的成本，使我们能够研究算法诊断筛查的益处，并促进开发更具临床相关性的算法。在挑战赛期间，我们收到了来自87个团队的779种算法，产生了53个用于从PCG记录中检测心脏杂音和心脏功能异常的有效代码库。这些算法代表了学术界和工业界的多种方法，包括使用具有工程临床和统计特征的更传统机器学习技术的方法，以及主要依赖深度学习模型来发现信息特征的方法。利用心音记录来识别心脏杂音和心脏功能异常，使我们能够探索算法方法在资源有限环境中提供更易获得的诊断筛查的潜力。提交有效的开源算法以及使用新颖的评估指标支持了挑战赛研究的可重复性、可推广性和临床相关性。
