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

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Reyna, MA (Author) Other; Kiarashi, Y (Author) Other; Elola, A (Author) Other; Oliveira, J (Author) Other; Renna, F (Author) FCUP; Gu, A (Author) Other; Perez Alday, EA (Author) Other; Sadr, N (Author) Other; Sharma, A (Author) Other; Silva Mattos, Sd (Author) Other; Coimbra, M (Author) FCUP; Sameni, R (Author) Other; Rad, AB (Author) Other; Clifford, GD (Author) Other

Conference proceedings International

Title: Computing in Cardiology, CinC 2022, Tampere, Finland, September 4-7, 2022

Pages: 1-4

2022 Computing in Cardiology, CinC 2022

4 September 2022 through 7 September 2022

Indexing

Scopus

Other information

Authenticus ID: P-00Y-8X4

DOI: 10.22489/cinc.2022.109

Abstract (EN): 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, open-source 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. © 2022 Creative Commons.

Language: English

Type (Professor's evaluation): Scientific