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Arian Ranjbar on Muck Rack

Arian Ranjbar

As seen in: Nature, Dove Medical Press
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Articles

Heart failure detection in electrocardiograms using Artificial Intelligence and pragmatic labelling

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By Jesper Ravn, Henrik Schirmer, Arian Ranjbar, Elias Stenhede
| Nature Verified
Abstract The diagnosis of heart failure (HF) is resource-intensive, leading to severe underdiagnosis. This study proposes the use of a deep learning model to detect HF solely from electrocardiograms. HF has limited validity in diagnosis codes, lowering the viability of direct application of supervised learning. However, validating the codes with measured levels of circulating N-terminal proB-type natriuretic peptide (NT-proBNP) during training mitigates the impact of label noise.
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Computer Science > Computer Vision and Pattern Recognition

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By Arian Ranjbar
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Digitizing Paper ECGs at Scale: An Open-Source Algorithm for Clinical Research

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By Agnar Martin, Agnar Martin Bjørnstad, Arian Ranjbar
| arXiv
arXiv:2510.19590 (cs) View PDF Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2510.19590 [cs.CV] (or arXiv:2510.19590v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2510.19590 Submission history From: Arian Ranjbar [ view email] [v1] Wed, 22 Oct 2025 13:41:21 UTC (40,938 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is...
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