Machine learning methods for determining skin age: A systematic review

Eric McMullen; Rokhshid Aflaki; Pranav Jignesh Khatri; Dea Metko; Kyle Storm; Abu Bakar Butt; Mahan Maazi; Raghav Gupta; Rajan Grewal; Trevor Champagne

doi:10.1016/j.jtv.2025.100887

Machine learning methods for determining skin age: A systematic review

J Tissue Viability. 2025 Mar 22;34(3):100887. doi: 10.1016/j.jtv.2025.100887. Online ahead of print.

Authors

Affiliations

¹ Division of Dermatology, Department of Medicine, University of Toronto, Toronto, ON, Canada. Electronic address: eric.mcmullen@medportal.ca.
² Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
³ Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
⁴ School of Health, University of Waterloo, Waterloo, ON, Canada.
⁵ Faculty of Medicine, University of British Columbia, BC, Canada.
⁶ Dermatology Division, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Women's College Research Institute, Women's College Hospital, Toronto, ON, Canada.

PMID: 40174294
DOI: 10.1016/j.jtv.2025.100887

Abstract

Aim: This systematic review explores how machine learning is used in determining skin aging, aiming to evaluate accuracy, limitations, and gaps in the current literature.

Materials and methods: OVID Embase, OVID Medline, IEEE Xplore, and ACM Digitial Library were searched from inception to March 16, 2024.

Results: A total of 1467 non-duplicate articles were screened, and 27 were ultimately included in the systematic review. The machine learning models exhibited a range of accuracies from a mean absolute error of 2.30-8.16 years. The most common approach was full facial image analysis, followed by non-image-based studies utilizing biomarkers such as the methylome and the proteome. The incorporation of dynamic facial expressions in the analysis was shown to improve the accuracy of age estimation, with a mean absolute error of 3.74. Confocal microscopy demonstrated potential for accurate skin aging estimation, with some studies achieving up to 85 % accuracy. Many studies were found with high PROBAST risk of bias scores, most commonly due to small sample sizes.

Conclusion: Future studies should aim for greater diversity in ethnicity and variables within datasets to improve generalizability.

Keywords: Artificial intelligence; Biomarker; Image analysis; Machine learning; Skin aging.

Publication types

Review