ArticleJournal of the European Academy of Dermatology and Venereology : JEADV2024
Deep learning predicted perceived age is a reliable approach for analysis of facial ageing: A proof of principle study.
Article in Journal of the European Academy of Dermatology and Venereology : JEADV, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Facial skin aging: an integrative analysis of genetics, epigenetics, and lifestyle factors.GeroScience · 2026Article
- Deep learning predicted perceived age is a reliable approach for analysis of facial ageing: A proof of principle study.Journal of the European Academy of Dermatology and Venereology : JEADV · 2024Article
- Insights into AI and facial ageing.Journal of the European Academy of Dermatology and Venereology : JEADV · 2024Article
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17 authors.
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Abstract
backgroundPerceived age (PA) has been associated with mortality, genetic variants linked to ageing and several age-related morbidities. However, estimating PA in large datasets is laborious and costly to generate, limiting its practical applicability.
objectivesTo determine if estimating PA using deep learning-based algorithms results in the same associations with morbidities and genetic variants as human-estimated perceived age.
methodsSelf-supervised learning (SSL) and deep feature transfer (DFT) deep learning (DL) approaches were trained and tested on human-estimated PAs and their corresponding frontal face images of middle-aged to elderly Dutch participants (n = 2679) from a population-based study in the Netherlands. We compared the DL-estimated PAs with morbidities previously associated with human-estimated PA as well as genetic variants in the gene MC1R; we additionally tested the PA associations with MC1R in a new validation cohort (n = 1158).
resultsThe DL approaches predicted PA in this population with a mean absolute error of 2.84 years (DFT) and 2.39 years (SSL). In the training-test dataset, we found the same significant (p < 0.05) associations for DL PA with osteoporosis, ARHL, cognition, COPD and cataracts and MC1R, as with human PA. We also found a similar but less significant association for SSL and DFT PAs (0.69 and 0.71 years per allele, p = 0.008 and 0.011, respectively) with MC1R variants in the validation dataset as that found with human, SSL and DFT PAs in the training-test dataset (0.79, 0.78 and 0.71 years per allele respectively; all p < 0.0001).
conclusionsDeep learning methods can automatically estimate PA from facial images with enough accuracy to replicate known links between human-estimated perceived age and several age-related morbidities. Furthermore, DL predicted perceived age associated with MC1R gene variants in a validation cohort. Hence, such DL PA techniques may be used instead of human estimations in perceived age studies thereby reducing time and costs.
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