Contrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies

Kaustubh Chakradeo, Pernille Nielsen, Lise Mette Rahbek Gjerdrum, Gry Sahl Hansen, David A Duchêne +3 more
11/25/2024
eess.IVcs.AIcs.CV

No official code found. Best implementation: sbhattlab/skin-biopsies-ageing-mortality (0 stars, last commit 2025-01-07).

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.

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Cite this paper

@article{chakradeo2024contrastive,
  title  = {Contrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies},
  author = {Kaustubh Chakradeo and Pernille Nielsen and Lise Mette Rahbek Gjerdrum and Gry Sahl Hansen and David A Duchêne and Laust H Mortensen and Majken K Jensen and Samir Bhatt},
  year   = {2024},
  eprint = {2411.16956},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.16956v1}
}

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