AI-Based Grading of Facial Aging Signs and Prediction of Visible Age From Standardized Facial Images: Algorithm Development and Internal Evaluation Study

Facial appearance influences visible age (VA), defined here as the age perceived by others; this perception-based outcome differs from chronological age and biological age. Human evaluation of facial aging signs can be time-consuming and subject to interobserver variability. A standardized AI-based approach might offer a more reproducible assessment of visible facial aging in digital dermatology research.

This study aimed to develop and evaluate an AI-based process for automatically grading facial aging signs and predicting the VA of participants from standardized full-face images.

Evaluation of full-face photographs of 468 North American women aged 18 to 80 years recruited in Dallas and Phoenix, Texas. Fifteen trained experts, including dermatologists, graded 13 aging signs using Skin Aging Atlas scales. A separate, naïve group of 253 North Americans aged 20 to 60 years, recruited in New York City, estimated the VA of each participant. For each sign, facial areas were pulled out using facial landmarks and fed into ResNet-based regression models, which were trained to match the averaged expert scores. The automated facial sign grading models were tested on 90% of the image set, and the remaining 10% was used for evaluation. Results were assessed using the mean absolute error, the proportions of estimates within 0.5, 1.0, and 1.5 grading units of the expert reference, and Spearman correlations between chronological age, human-assessed VA, and predicted visible age (PVA).

The mean absolute error was below 0.3 grading units for 11 of the 13 facial aging signs. The average proportion of automatic gradings within 1 grading unit of the expert reference was about 98% for participants of East Asian ancestry group, 96% for participants of African ancestry group, and 97% for participants of European ancestry group. Human-assessed VA correlated strongly with real age (r=0.94, 95% CI 0.928-0.950; P<.001), although participants were perceived as around 10 years older than their real age, on average. The AI-PVA also correlated strongly with the human-assessed VA (r=0.93, 95% CI 0.917-0.941; P<.001), with approximately 70% of estimates within 5 years.

The AI-based system showed low grading errors across most facial aging signs and also a fairly strong relationship between the PVA and human-assessed VA under standardized image acquisition conditions. Consequently, the system could provide a reproducible digital outcome for facial aging research and future longitudinal monitoring. However, external validation across separate populations, in men, and across image acquisition devices is required before clinical and consumer-facing implementation. As the predicted visible age is derived from quantified clinical facial signs, it reflects measurable features of visible facial aging, while its relationship with biological age and overall health remains to be established through future studies integrating biological biomarkers.