Exploring artificial intelligence from a clinical perspective: A comparison and application analysis of two facial age predictors trained on a large-scale Chinese cosmetic patient database.

BACKGROUND: Age prediction powered by artificial intelligence (AI) can be used as an objective technique to assess the cosmetic effect of rejuvenation surgery. Existing age-estimation models are trained on public datasets with the Caucasian race as the main reference, thus they are impractical for clinical application in Chinese patients. METHODS: To develop and select an age-estimation model appropriate for Chinese patients receiving rejuvenation treatment, we obtained a face database of 10 529 images from 1821 patients from the author's hospital and selected two representative age-estimation algorithms for the model training. The prediction accuracies and the interpretability of calculation logic of these two facial age predictors were compared and analyzed. RESULTS: The mean absolute error (MAE) of a traditional support vector machine-learning model was 10.185 years; the proportion of absolute error ≤6 years was 35.90% and 68.50% ≤12 years. The MAE of a deep-learning model based on the VGG-16 framework was 3.011 years; the proportion of absolute error ≤6 years was 90.20% and 100% ≤12 years. Compared with deep learning, traditional machine-learning models have clearer computational logic, which allows them to give clinicians more specific treatment recommendations. CONCLUSION: Experimental results show that deep-learning exceeds traditional machine learning in the prediction of Chinese cosmetic patients' age. Although traditional machine learning model has better interpretability than deep-learning model, deep-learning is more accurate for clinical quantitative evaluation. Knowing the decision-making logic behind the accurate prediction of deep-learning is crucial for deeper clinical application, and requires further exploration.

Blepharoplasty Combined With Mini-incision and Interrupted Buried Suture Techniques.

BACKGROUND: Blepharoplasty has become one of the most popular plastic surgery techniques for generating double-eyelid folds. The mini-incision blepharoplasty technique results in minimal trauma and the formation of supratarsal folds. METHODS: In this study, the authors combined mini-incisions with interrupted buried sutures. To perform the blepharoplasty procedure, the authors marked the supratarsal folds and divided them into 5 line segments: 3 cutting lines and 2 noncutting lines. For the cutting lines, the authors used orbicularis-tarsus fixation to form double eyelids and only removed a small strip of muscle under the incision to maintain the physiological structure of the pretarsal tissue. For the noncutting lines, the authors used the interrupted buried suture technique to add 2 fixed points. RESULTS: A total of 42 patients (mean age 25.25 years) underwent this minimally invasive blepharoplasty. Among these patients, 42 underwent bilateral surgery. The average follow-up period was 35.91 months (range: 13-47 months). Only one patient underwent a second operation to address a shallow, unilateral supratarsal crease. Nonetheless, all patients were satisfied with their results. CONCLUSION: Our minimally invasive blepharoplasty approach resulted in minimal damage to the pretarsal tissues and robust supratarsal folds and is relatively easy to perform for the novice surgeon. EVIDENCE STATEMENT: Level IV.