Lence A. auteur aut IRD Fall Ahmad auteur aut IRD Cohen S. D. auteur aut IRD Granese F. auteur aut IRD Zucker Jean-Daniel auteur aut IRD Salem J. E. auteur aut IRD Prifti Edi auteur aut IRD text journalArticle eng text/pdf reformatted digital access Background and Objective: Electrocardiograms (ECGs) are essential for diagnosing cardiac pathologies, yet traditional paper-based ECG storage poses significant challenges for automated analyses. Despite the growing interest in leveraging AI for ECG analysis, there remains a lack of accessible, fully automated tools for digitizing paper-based ECGs. Existing solutions are often incomplete, behind paywalls, or not suited for large-scale use. To address this gap, we present ECGTIzER: an open-source, fully automated tool that enables high-fidelity digitization of paper ECGs, ensuring long-term preservation of clinical data and unlocking their potential for modern AI-driven analysis. Methods: ECGTIzER employs automated lead detection, three different pixel-based signal extraction algorithms, and a deep learning-based signal reconstruction module. We evaluated ECGTIzER on two datasets: a real-life cohort from the COVID-19 pandemic (JOCOVID) and a publicly available dataset (PTB-XL). Performance was compared with two existing methods: the fully automated ECGMINER and the semi-automated PAPERECG, which requires human intervention. The tools' digitization performance was assessed in terms of signal recovery, the fidelity of clinically relevant feature measurement and downstream AI classification tasks on a third dataset (GENEREPOL). Results: Results show that ECGTIzER outperforms state-of-the-art methods, with its ECGTIzERFrag algorithm delivering superior signal recovery performance. While PAPERECG demonstrated better outcomes than ECGMINER, it also requires human input. Conclusions: ECGTIzER enhances the usability of historical ECG data and supports advanced AI-based diagnostic methods, making it a valuable addition to the field of AI in ECG analysis. specialized Electrocardiograms Digitization Signal recovery Fully automated Torsades-de-pointes Artificial intelligence 122 050 112 D 108710 [12 p.] 2026 1746-8094 https://www.documentation.ird.fr/hor/fdi:010095306 10.1016/j.bspc.2025.108710 1746-8094 [F B010095306] https://www.documentation.ird.fr/hor/fdi:010095306 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2025-11/010095306.pdf IRD - Base Horizon / Pleins textes 2025-11-18 2025-11-18 fdi:010095306 fre