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Predicting senescence across tissues and species using deep learning

Deep learning was applied to cellular images to predict senescence on the basis of nuclear morphology. These methods recognize senescence in diverse cell types, show increasing senescence with age in liver and dermis, and suggest that higher rates of senescence associate with several age-related diseases but reduced cancer risk.

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Fig. 1: Predicting senescence.


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This is a summary of: Heckenbach, I. et al. Nuclear morphology is a deep learning biomarker of cellular senescence. Nat. Aging (2022)

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Predicting senescence across tissues and species using deep learning. Nat Aging 2, 688–689 (2022).

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