Estimating apparent age using artificial intelligence: Quantifying the effect of blepharoplasty

Abstract

Objectives: Quantify the rejuvenation effect of blepharoplasty.

Methods: A dataset of facial photographs was assembled and randomly split into 90% training and 10% validation sets. An artificial intelligence model was trained to input a facial photograph and output the apparent age of the depicted face. A retrospective chart review of patients who underwent blepharoplasty was used to assemble a test set-preoperative and postoperative photographs were culled and subsequently analyzed by the model.

Results: A total of 47394 images of patients aged 26-89 years old were used for model training and validation. On the validation set, the model achieved 75% accuracy with a mean absolute error of 1.38 years and Pearson's r of 0.92. A total of 103 patients (29 males and 74 females) met the test set inclusion criteria (upper blepharoplasty n = 28, lower blepharoplasty n = 33, and quadrilateral blepharoplasty n = 42). The test set age ranged from 30.3 to 83.8 years old (mean 60.8, standard deviation 11.4). Overall, the model-predicted test set patients to be 0.74 years younger preoperatively versus 2.52 years younger postoperatively (p < 0.01). Significant underestimation of age was observed in women who underwent lower blepharoplasty (n = 23, 1.28 years older preoperatively vs. 2.32 years younger postoperatively, p = 3.8 × 10^-4) and men who underwent quadrilateral blepharoplasty (n = 10, 0.71 years younger preoperatively vs. 5.34 years younger postoperatively, p = 0.02).

Conclusions: The deep learning algorithm developed in this study demonstrates that, on average, blepharoplasty provides a rejuvenating effect of approximately 2 years.

Keywords: Artificial intelligence; Blepharoplasty; Deep learning; Rejuvenation.