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

2023; Elsevier BV; Volume: 85; Linguagem: Inglês

10.1016/j.bjps.2023.07.017

1878-0539

Kendall Goodyear, Persiana S. Saffari, Mahtash Esfandiari, Samuel Baugh, Daniel B. Rootman, Justin Karlin,

Orthodontics and Dentofacial Orthopedics

Quantify the rejuvenation effect of blepharoplasty.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.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).The deep learning algorithm developed in this study demonstrates that, on average, blepharoplasty provides a rejuvenating effect of approximately 2 years.