Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022

心音图 乔治(机器人) 计算机科学 语音识别 人工智能
作者
Matthew A. Reyna,Yashar Kiarashi,Andoni Elola,Jorge Oliveira,Francesco Renna,Annie Gu,Nadi Sadr,Erick Andres Perez Alday,Ashish Sharma,Sandra da Silva Mattos,Miguel Coimbra,Reza Sameni,Ali Bahrami Rad,Gari Clifford"
出处
期刊:Computing in Cardiology (CinC), 2012 被引量:9
标识
DOI:10.22489/cinc.2022.109
摘要

The George B. Moody PhysioNet Challenge 2022 explored the detection of abnormal heart function from phonocardiogram (PCG) recordings.Although ultrasound imaging is becoming more common for investigating heart defects, the PCG still has the potential to assist with rapid and low-cost screening, and the automated annotation of PCG recordings has the potential to further improve access.Therefore, for this Challenge, we asked participants to design working, opensource algorithms that use PCG recordings to identify heart murmurs and clinical outcomes.This Challenge makes several innovations.First, we sourced 5272 PCG recordings from 1568 patients in Brazil, providing high-quality data for an underrepresented population.Second, we required the Challenge teams to submit working code for training and running their models, improving the reproducibility and reusability of the algorithms.Third, we devised a cost-based evaluation metric that reflects the costs of screening, treatment, and diagnostic errors, facilitating the development of more clinically relevant algorithms.A total of 87 teams submitted 779 algorithms during the Challenge.These algorithms represent a diversity of approaches from both academia and industry for detecting abnormal cardiac function from PCG recordings.

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