Paper ID: 2302.10301

Artificial Intelligence System for Detection and Screening of Cardiac Abnormalities using Electrocardiogram Images

Deyun Zhang, Shijia Geng, Yang Zhou, Weilun Xu, Guodong Wei, Kai Wang, Jie Yu, Qiang Zhu, Yongkui Li, Yonghong Zhao, Xingyue Chen, Rui Zhang, Zhaoji Fu, Rongbo Zhou, Yanqi E, Sumei Fan, Qinghao Zhao, Chuandong Cheng, Nan Peng, Liang Zhang, Linlin Zheng, Jianjun Chu, Hongbin Xu, Chen Tan, Jian Liu, Huayue Tao, Tong Liu, Kangyin Chen, Chenyang Jiang, Xingpeng Liu, Shenda Hong

The artificial intelligence (AI) system has achieved expert-level performance in electrocardiogram (ECG) signal analysis. However, in underdeveloped countries or regions where the healthcare information system is imperfect, only paper ECGs can be provided. Analysis of real-world ECG images (photos or scans of paper ECGs) remains challenging due to complex environments or interference. In this study, we present an AI system developed to detect and screen cardiac abnormalities (CAs) from real-world ECG images. The system was evaluated on a large dataset of 52,357 patients from multiple regions and populations across the world. On the detection task, the AI system obtained area under the receiver operating curve (AUC) of 0.996 (hold-out test), 0.994 (external test 1), 0.984 (external test 2), and 0.979 (external test 3), respectively. Meanwhile, the detection results of AI system showed a strong correlation with the diagnosis of cardiologists (cardiologist 1 (R=0.794, p<1e-3), cardiologist 2 (R=0.812, p<1e-3)). On the screening task, the AI system achieved AUCs of 0.894 (hold-out test) and 0.850 (external test). The screening performance of the AI system was better than that of the cardiologists (AI system (0.846) vs. cardiologist 1 (0.520) vs. cardiologist 2 (0.480)). Our study demonstrates the feasibility of an accurate, objective, easy-to-use, fast, and low-cost AI system for CA detection and screening. The system has the potential to be used by healthcare professionals, caregivers, and general users to assess CAs based on real-world ECG images.

Submitted: Feb 10, 2023