Abstract Background Most artificial intelligence-enhanced ECG (AI-ECG) models used to predict adverse events including death require that the ECGs be stored digitally. However, the majority of clinical facilities worldwide store ECGs as images. Methods 1,163,401 ECGs (189,539 patients) from a secondary care dataset were available as both natively digital traces and PDF images. A digitisation pipeline extracted signals from PDFs. Separate 1D convolutional neural network (CNN) models were trained on natively digital or digitised ECGs, with a discrete-time survival loss function to predict time-to-mortality. A 2D CNN model was trained on 310x868 pixel ECG images. External validation was performed in 958,954 ECGs (645,373 patients) from a Brazilian primary care cohort and 1022 ECGs (1022 patients) from a Chagas disease cohort. Results The image 2D CNN model and digitised 1D CNN model performed comparably to natively digital 1D CNN model in internal (C-index 0.780 (0.779-0.781), 0.772 (0.771-0.774) and 0.775 (0.774-0.776 respectively) and external validation. Models trained on natively digital 1D ECGs had comparable performance when applied to digitised 1D ECGs (C-index 0.773 (0.771-0.774). Conclusion Both the image 2D CNN and digitised 1D CNN enable mortality prediction from ECG images, with comparable performance to natively digital 1D CNN. Models trained on natively digital 1D ECGs can also be applied to digitised 1D ECGs, without any significant loss in performance. This works allows AI-ECG mortality prediction to be applied in diverse global settings lacking digital ECG infrastructure.
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