J Cosmet Med.  2023 Dec;7(2):60-65. 10.25056/JCM.2023.7.2.60.

Artificial-intelligence-automated machine learning as a tool for evaluating facial rhytid images

• Espaillat A

• KMID: 2561180
• DOI: http://doi.org/10.25056/JCM.2023.7.2.60

Abstract

Background
The growing demand for nonsurgical cosmetic treatments necessitates a reliable diagnostic tool to assess the extent of aging, severity of facial wrinkles, and effectiveness of minimally invasive aesthetic procedures. This is crucial to accurately predict the need for botulinum neurotoxin type A neuromodulator injections during facial aesthetic rejuvenation.
Objective
This study aimed to determine the accuracy of artificial intelligence-based machine learning algorithms in analyzing facial rhytid images during facial aesthetic evaluation.
Methods
A prospective validation model was implemented using a dataset of 3,000 de-identified facial rhytid images from 600 patients in a community private medical spa aesthetic screening program. A neural architecture based on Google Cloud’s artificial intelligence-automated machine learning was developed to detect dynamic hyperkinetic skin lines in various facial muscles. Images were captured using a handheld iPad camera and labeled by an American board-certified ophthalmologist using established quantitative grading scales. The dataset was divided into training (80%), validation (10%), and testing (10%) sets. The model’s performance was evaluated using the following metrics: area under the precision–recall curve, sensitivity, specificity, precision, and accuracy.
Results
Facial rhytid images were detected in 79.9%, 10.7%, and 9.3% of the training sets, respectively. The model achieved an area under the precision–recall curve of 0.943, with an accuracy of 91.667% and a recall of 81.881% at a threshold score of 0.5.
Conclusion
This study demonstrates the successful application of artificial-intelligence-automated machine learning in identifying facial rhytid images captured using simple photographic devices in a community-based private medical spa program. Thus, the potential value of machine-learning algorithms for evaluating the need for minimally invasive injectable procedures for facial aesthetic rejuvenation was established.

Keyword

artificial intelligence; BoNT/A neuromodulator injections; facial aesthetic rejuvenation; facial rhytid images; machinelearning algorithms