A new, objective approach can help estimate the age after a facelift operation


For most patients, the reasons for a facelift are simple: “turn back the clock” for a younger and more attractive look. Even in the 2020 pandemic year, according to statistics from the American Society of Plastic Surgeons (ASPS), more than 234,000 patients underwent facelift surgery.

When considering facelift surgery, patients may wonder, “How much younger will I look?” This question is difficult to answer for plastic surgeons. Typically, the cosmetic results of a facelift were assessed on a case-by-case basis or based on subjective evaluations.

Now research is proposing a new, objective approach to assessing the reduction in apparent age after facelift surgery: artificial intelligence (AI) networks trained to estimate age based on facial photos.

“Our study shows that currently available AI algorithms can detect the success of facelifts and even quantify the reduction in perceived age in years,” comments senior author James P. Bradley, MD, Vice Chairman of Surgery, Zucker School of Medicine in Hofstra / Northwell. The study appears in the July issue of Plastic and Reconstructive Surgery®, the official medical journal of the ASPS.

The study used a type of AI algorithm called convolutional neural networks.

By training on data sets containing millions of public images, these neural networks can learn to recognize facial features with much more ‘experience’ than a typical person.

James P. Bradley, MD, Study Senior Author and Vice Chairman, Surgery, Zucker School of Medicine, Hofstra / Northwell

Four different publicly available neural networks were used to make objective age estimates of facial age for 50 patients undergoing facelifts. The AI ​​estimates were made using standardized photos taken before and at least one year after the facelift surgery. The results were compared with the patients’ subjective ratings of their appearance along with the responses to a standard patient assessment (FACE-Q questionnaire).

The patients were all women, mean age 58.7 years. The AI ​​algorithms used in the study were 100 percent accurate in identifying patients’ ages based on “before” photos.

In the “after” photos, the neural networks recognized a 4.3-year reduction in age after the facelift operation. That was significantly less than the 6.7-year shortening that the patients themselves estimated. “Patients tend to overestimate how much younger they look after facelift surgery – which may reflect their emotional and financial investment in the procedure,” comments Dr. Bradley.

On the FACE-Q questionnaire, patients were very satisfied with the results of their facelift surgery: the average score (on a scale from 0 to 100) was 75 for facial appearance and over 80 for quality of life. The neural network estimates of age reduction correlated directly with patient satisfaction. “The younger the AI ​​program perceives a patient’s age, the more satisfied they are with the results of their facelift,” says Dr. Bradley.

Artificial intelligence algorithms can provide an objective and reliable estimate of the apparent age reduction after a facelift operation, according to the new findings. These age estimates also seem to provide an indicator of patient satisfaction – even if the reduction in years does not quite correspond to the patient’s subjective assessment.

“Along with powerful image analysis tools used in modern plastic surgery, neural networks can play a useful role in advising patients and demonstrating successful results from facial rejuvenation procedures,” adds Dr. Bradley added. “We believe that AI algorithms could also play a useful role for plastic surgeons to evaluate their own results and to compare the results of different techniques.”


Journal reference:

Zhang, B., et al. (2021) Turning back time: Artificial intelligence that detects the age reduction after a facelift operation correlates with patient satisfaction. Plastic and Reconstructive Surgery. doi.org/10.1097/PRS.0000000000008020.


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