  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.
