Multiple high-regional-incidence cardiac disease diagnosis with deep learning and its potential to elevate cardiologist performance

Yunqing Liu; Chengjin Qin; Chengliang Liu; Jinlei Liu; Yanrui Jin; Zhiyuan Li; Liqun Zhao

doi:10.1016/j.isci.2022.105434

Multiple high-regional-incidence cardiac disease diagnosis with deep learning and its potential to elevate cardiologist performance

iScience. 2022 Oct 26;25(11):105434. doi: 10.1016/j.isci.2022.105434. eCollection 2022 Nov 18.

Authors

Yunqing Liu^{1

2}, Chengjin Qin^{1

2}, Chengliang Liu^{1

2}, Jinlei Liu^{1

2}, Yanrui Jin^{1

2}, Zhiyuan Li^{1

2}, Liqun Zhao³

Affiliations

¹ School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai 200240, China.
² MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China.
³ Department of Cardiology, Shanghai First People's Hospital Affiliated to Shanghai Jiao Tong University, 100, Haining Road, Shanghai 200080, China.

Abstract

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.

Keywords: Biological sciences; Cardiovascular medicine; Diagnostics.