Horizon 1 1 17 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES Biomedical Signal Processing and Control 108710 [12 p.] Electrocardiograms Digitization Signal recovery Fully automated Torsades-de-pointes Artificial intelligence 2026 fdi:010095306 ENG Biomedical Signal Processing and Control 1746-8094 ISI:001584508700001 D 10.1016/j.bspc.2025.108710 https://www.documentation.ird.fr/hor/fdi:010095306 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2025-11/010095306.pdf 112 Horizon (IRD) 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. 122 ; 050 UR209