Year: 2024
Type: preprint
Study design: Observational Study
Abstract: As global life expectancy increases, so does the burden of chronic diseases, yet individuals exhibit considerable variability in the rate at which they age. Identifying biomarkers that distinguish fast from slow ageing is crucial for understanding the biology of ageing, enabling early disease detection, and improving prevention strategies. Using contrastive deep learning, we show that skin biopsy images alone are sufficient to determine an individual's age. We then use visual features in histopathology slides of the skin biopsies to construct a novel biomarker of ageing. By linking with comprehensive health registers in Denmark, we demonstrate that visual features in histopathology slides of skin biopsies predict mortality and the prevalence of chronic age-related diseases. Our work highlights how routinely collected health data can provide additional value when used together with deep learning, by creating a new biomarker for ageing which can be actively used to determine mortality over time.
Source: arXiv (Cornell University)
Authors: Chakradeo, Kaustubh, Nielsen, Pernille, Gjerdrum, Lise Mette Rahbek, Hansen, Gry Sahl, Duchêne, David A, Mortensen, Laust H, Jensen, Majken K, Bhatt, Samir
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Villum Fonden, London School of Hygiene and Tropical Medicine, National Institute for Health Research Health Protection Research Unit, National Institute for Health and Care Research, Department of Health and Social Care, Imperial College London, National Research Foundation, Danmarks Grundforskningsfond, Novo Nordisk, NordForsk, Novo Nordisk Fonden, William Demant Fonden, Medical Research Council, European and Developing Countries Clinical Trials Partnership
OpenAlex ID: W4404988293