Journal of tissue viability · 2025
Machine learning methods for determining skin age: A systematic review.
СтатьяСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
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McMullen Eric, Aflaki Rokhshid, Khatri Pranav Jignesh, Metko Dea, Storm Kyle, Butt Abu Bakar, Maazi Mahan, Gupta Raghav, Grewal Rajan, Champagne Trevor
Аннотация
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.
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.