Machine learning methods for determining skin age: A systematic review

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

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

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.

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