Algorithms can reliably estimate reductions in apparent age after face-lift surgery; reduction correlates with patient satisfaction
WEDNESDAY, July 7, 2021 (HealthDay News) — Artificial intelligence algorithms can estimate the reduction in apparent age following face-lift surgery, and this reduction is associated with patient satisfaction, according to a study published in the July issue of Plastic and Reconstructive Surgery.
Ben H. Zhang, from the Zucker School of Medicine at Hofstra/Northwell in Uniondale, New York, and colleagues used standardized preoperative and postoperative (one year) images of 50 consecutive patients who underwent face-lift procedures to estimate age reduction after surgery by four neural networks trained to identify age based on facial features. Patient-reported facial esthetic outcome was measured using FACE-Q surveys.
The researchers found that all four neural networks were accurate in identifying ages based on the neural network preoperative age accuracy score. A greater age reduction was reported in patient self-appraisal age reduction than in neural network age reduction after a face lift (−6.7 versus −4.3 years). High levels of patient satisfaction for facial appearance, quality of life, and satisfaction with outcome were demonstrated in FACE-Q scores. A positive correlation was seen between neural age reduction and patient satisfaction.
“Artificial intelligence through convolutional neural networks recognizes the success of face-lift surgery in reducing the perceived age of patients,” the authors write. “With powerful image analysis readily available, neural networks may assist in counseling patients and demonstrate postoperative success.”
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