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Multiple high-regional-incidence cardiac disease diagnosis with deep learning and its potential to elevate cardiologist performance
- Source :
- iScience, Vol 25, Iss 11, Pp 105434- (2022)
- Publication Year :
- 2022
- Publisher :
- Elsevier, 2022.
-
Abstract
- Summary: Currently, due to lack of large-scale datasets containing multiple arrhythmias and acute coronary syndrome-related diseases, AI-aided diagnosis for cardiac diseases is limited in clinical scenarios. Whether AI-based ECG diagnosis can assist cardiologists to improve performance has not been reported. We constructed a large-scale dataset containing multiple high-regional-incidence arrhythmias and ACS-related diseases, including 162,622 12-lead ECGs collected between January 2018 and March 2021. We presented a deep learning model for clinical ECG diagnosis of multiple cardiac diseases. Results show that our model for diagnosing 15 cardiac abnormalities achieved 88.216% accuracy, and its average AUC ROC score reached 0.961. On the board-certified re-annotated dataset, its performance surpasses that of cardiologists in non-reference group. Moreover, with aid of labels given by our model, accuracy and efficiency for cardiologist increased by 13.5% and 69.9% than non-reference group. Our approach provides solutions for AI-aided diagnosis systems of cardiac diseases in applications.
- Subjects :
- Cardiovascular medicine
Diagnostics
Biological sciences
Science
Subjects
Details
- Language :
- English
- ISSN :
- 25890042
- Volume :
- 25
- Issue :
- 11
- Database :
- Directory of Open Access Journals
- Journal :
- iScience
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.02e6ddcea993467b81793b10374a9492
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.isci.2022.105434