AI-driven preclinical disease risk assessment using imaging in UK biobank.

Dmitrii Seletkov^1,2, Sophie Starck³, Tamara T Mueller³, Yundi Zhang³, Lisa Steinhelfer⁴, Daniel Rueckert^3,5, Rickmer Braren^4,6

¹Institute of Diagnostic and Interventional Radiology, Technical University of Munich School of Medicine, Munich, Germany. dmitrii.seletkov@tum.de.

²Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany. dmitrii.seletkov@tum.de.

³Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany.

⁴Institute of Diagnostic and Interventional Radiology, Technical University of Munich School of Medicine, Munich, Germany.

⁵Department of Computing, Imperial College London, London, UK.

⁶German Cancer Consortium (DKTK), Munich partner site, Heidelberg, Germany.

PubMed PMC DOI PDF

Abstract

Identifying disease risk and detecting disease before clinical symptoms appear are essential for early intervention and improving patient outcomes. In this context, the integration of medical imaging in a clinical workflow offers a unique advantage by capturing detailed structural and functional information. Unlike non-image data, such as lifestyle, sociodemographic, or prior medical conditions, w…

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