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We introduce a fully automated, modular framework that converts scanned or photographed ECGs into digital signals, suitable for both clinical and research applications. The framework is validated on 37,191 ECG images with 1596 collected at Akershus University Hospital, where the algorithm obtains a mean signal-to-noise ratio of 19.65\u2009dB on scanned papers with common artifacts. It is further evaluated on the Emory Paper Digitization ECG Dataset, comprising 35,595 images, including images with perspective distortion, wrinkles, and stains. The model improves on the state-of-the-art in all subcategories. The full software is released as open-source, promoting reproducibility and further development. We hope the software will contribute to unlocking retrospective ECG archives and democratize access to AI-driven diagnostics.<\/jats:p>","DOI":"10.1038\/s41746-025-02327-1","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T06:04:06Z","timestamp":1768370646000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Digitizing paper ECGs at scale: an open-source algorithm for clinical research"],"prefix":"10.1038","volume":"9","author":[{"given":"Elias","family":"Stenhede","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Agnar Martin","family":"Bj\u00f8rnstad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arian","family":"Ranjbar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,14]]},"reference":[{"key":"2327_CR1","unstructured":"Global, regional, and national burden of cardiovascular diseases and risk factors in 204 countries and territories, 1990-2023. 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