בשל "הגנת זכויות יוצרים" מובא להלן קישור לתקציר המאמר. לקריאתו בטקסט מלא, אנא פנה/י לספרייה הרפואית הזמינה לך. 

Patients desire face-lifting procedures primarily to appear younger, more refreshed, and attractive.

Because there are few objective studies assessing the success of face-lift surgery, the authors used artificial intelligence, in the form of convolutional neural network algorithms alongside FACE-Q patient-reported outcomes, to evaluate perceived age reduction and patient satisfaction following face-lift surgery.

Standardized preoperative and postoperative (1 year) images of 50 consecutive patients who underwent face-lift procedures (platysmaplasty, superficial musculoaponeurotic system–ectomy, cheek minimal access cranial suspension malar lift, or fat grafting) were used by four neural networks (trained to identify age based on facial features) to estimate age reduction after surgery.

In addition, FACE-Q surveys were used to measure patient-reported facial aesthetic outcome. Patient satisfaction was compared to age reduction.