The global aesthetic market has witnessed remarkable growth in recent decades, with an evident increase in the annual number of aesthetic procedures performed [1]. According to a survey by the American Society of Plastic Surgeons, the top 5 minimally invasive cosmetic procedures sought by patients in 2021–2022, ranked in the order of priority, were botulinum neurotoxin type A (BoNT/A), soft tissue fillers, non-invasive fat reduction, non-surgical skin tightening, and skin care treatments [1]. However, since the onset of the coronavirus disease-2019 pandemic, the demand for cosmetic procedures has increased to unprecedented levels. The same survey revealed that over 76% of cosmetic surgeons reported a surge in patients seeking these procedures. This was mainly attributed to individuals choosing to expend their travel budget on cosmetic enhancements rather than traveling. Moreover, several patients were willing to pay higher fees to enhance their self-esteem and confidence because they aspired to regain their sense of normalcy following the pandemic. Since the year 2000, procedures involving BoNT/A and soft tissue dermal fillers have increased by 459% and 174%, respectively, with an estimated total expenditure of over 2 billion dollars, particularly in 2020 [2].

Facial wrinkles, known as rhytids, are considered the most noticeable signs of facial skin aging. However, these wrinkles can appear for many other reasons [3]. These wrinkles are attributable to repeated contractions of the underlying facial muscles, resulting in thinning of the outer skin layer and the formation of creases. BoNT/A injections are commonly used to chemically relax specific facial muscles to rejuvenate the face and combat the signs of aging. Selective chemodenervation of these muscles provides a targeted treatment of dynamic wrinkles, thus offering a minimally invasive approach for facial rejuvenation [4]. The growing demand for non-surgical cosmetic treatments has led to the introduction of validated and non-validated scales to assess the extent of aging, severity of facial wrinkles, and effectiveness of minimally invasive aesthetic procedures [5,6].

Artificial intelligence (AI) is a rapidly advancing field of computer science that enables computers to learn from large data sets. This study demonstrated a significant breakthrough in expanding the application of AI in oculoplastic surgery, contrary to the current applications that are limited to radiological studies and medical records [7]. In addition, this study demonstrated the potential of an AI-based machine learning model as a reliable tool for evaluating facial rhytid images and accurately predicting the need for BoNT/A neuromodulator injections during facial aesthetic rejuvenation.

This study aimed to develop and validate an AI-automated machine-learning (AI-AutoML) model for classifying dynamic wrinkles using handheld images. We used 3,000 facial images of 600 patients obtained from a privately owned medical spa in South Florida, United States. The images were de-identified for the study. All the images were captured using an iPad Pro (OS V.16.5) (Apple Inc., Cupertino, CA, USA). The study adhered to the ethical standards outlined in the 1964 Declaration of Helsinki and the institutional review board approved the study protocol in compliance with the Health Insurance Portability and Accountability Act of 1996 (IRB no. #1119).

Wrinkle severity grading: facial imaging protocol

A facial image database was developed using previously validated grading scales for everyday clinical use [8-11]. These scales were designed to assess the outcomes of BoNT/A treatment in clinical trials and consist of a 5-point rating system. An American board-certified ophthalmologist identified anatomical changes caused by aging and incorporated them into the photographs. These images accurately displayed the steps of variation in the desired aspect. Both static and dynamic images were recorded to capture facial lines caused by different muscle group movements. However, only dynamic photographs were included in the study. Dynamic wrinkles were graded on a scale of 0 to 4 as follows: 0 (none), 1 (minimal), 2 (mild), 3 (moderate), and 4 (severe). This study focused on dynamic wrinkles graded 3 or 4 in which the expression of facial lines was maximized, thus improving the training, validation, and testing of the proposed AI-AutoML model dataset (Fig. 1).

Figure 1. Dynamic wrinkle images graded from 3 (moderate) to 4 (severe). (A) Corrugator lines; (B) orbicularis oris lines; (C) mentalis lines; (D) orbicularis oculi lines; (E) nasalis lines; (F) frontalis lines; (G) procerus lines.

AI-AutoML

Fig. 2 presents a flowchart outlining the segmentation process applied to the initial facial photograph dataset comprising 3,000 images. Initially, the images were classified into the following three categories: ungradable photos (112; 3.73%), pictures rated between 0 and 2 (2,446; 81.53%), and photos rated between 3 and 4 (442; 14.73%). Ungradable images and those rated between 0 and 2 were excluded from further analysis, and the remaining pictures rated as 3 or 4 were assessed using the AI-AutoML platform to evaluate the image quality. The image quality assessment feature is an integral component of the AI-AutoML platform and includes checks for significant blurring and contrast problems. Consequently, 153 images were deemed unsuitable and rejected.

Figure 2. Dataset splits for artificial intelligence-automated machine learning (AI-AutoML) using a handheld iPad (Apple Inc., Cupertino, CA, USA) camera. The mechanical AI model assessment evaluated the image quality for blue, noise, artifacts, contrast, and distortion, and 153 images were deemed unsuitable to be included in the model. A total of 289 images were considered suitable by the AI-AutoML platform and utilized for the model’s training, validation, and testing.

A total of 289 images were deemed suitable by the AI-AutoML platform and used for model testing, validation, and training. The selected photographs were uploaded to Google Cloud’s Vertex AI-AutoML platform (Alphabet Inc., Mountain View, CA, USA) to construct and deploy the models. The platform recommends 8 node hours for this process. The images were randomly divided into the following 3 sets: training (231; 80%), validation (31; 10%), and testing (27; 10%). AI-AutoML efficiently managed data pre-processing and selected the best network structure and parameters to accurately detect patterns in the training subset. The algorithm was fine-tuned using the validation set and the performance and effectiveness of the model were evaluated using the testing set.

Statistical analyses

The AI-AutoML platform provided a comprehensive statistical analysis of the model’s performance using the testing set. The AI model is a binary classifier that predicts categories based on the assigned probabilities of each image. This algorithm employs confidence thresholds that represent the minimum confidence levels required to classify images into specific categories. To assess the certainty of the model, a confidence score was assigned to each prediction, indicating that the predicted label was correct. The confidence threshold returned positive predictions if the confidence score was more significant than or equal to the selected value.

A higher confidence threshold increases precision but lowers recall, and vice versa. To determine the overall accuracy of the model, the area under the precision–recall curve (AUPRC) was computed by evaluating the testing set at confidence levels ranging from 0.0 to 1.0. A higher value indicated a higher quality model. A confidence threshold of 0.5 was applied in this study. The AUPRC demonstrates a trade-off between precision and recall when considering varying confidence thresholds. A lower threshold tends to yield higher recall but lower precision, whereas a higher threshold often results in lower recall but higher accuracy. Each point on the curve represents a pair of precision–recall values at different confidence thresholds. The precision–recall metrics assess the performance of the model for the highest-ranking label across various confidence threshold values.

It is difficult to determine the correct training sample size for machine learning models applied to medical imaging data, which studies have shown to be unknown most of the time [12]. Vertex AI-AutoML suggests using at least 100 images per category for an ideal training set; however, a minimum of 27 images in most classes can still be effectively used to train the model used in this study and accurately identify each label.

The distribution of facial images in the training set was as follows: 16.2%, 20.4%, 12.4%, 19.7%, 9.3%, 16.2%, and 7.9% for the corrugator, orbicularis oris, mentalis, orbicularis oculi, nasalis, frontalis, and procerus lines, respectively. The model achieved an AUPRC of 0.943, with a precision of 91.7% and recall of 81.5% when using a confidence threshold of 0.5 (Fig. 3). An increase in the confidence threshold resulted in a decrease in recall and an increase in precision. When evaluating the 10% testing set, the confusion matrix showed that all images with dynamic lines from the corrugator, orbicularis oris, mentalis, orbicularis oculi, and nasalis muscles were accurately categorized (100% true positives and an average precision of 0.999). However, images with dynamic lines from the frontalis and procerus muscles had favorable accuracy rates of 75% and 50%, respectively, and false-negative rates of 25% and 50%, with an average precision of 0.804 and 0.417, respectively (Table 1).

Table 1 . Model dataset percentages of precision, recall, distribution, number of images, and confidence threshold.

	Average precision	Precision (%)	Recall (%)	Distribution (%)	Images dataset
CL	0.999	100	80	16.2	47
OorL	0.999	100	100	20.4	59
ML	0.999	100	66.7	12.4	36
OocL	0.999	83.3	100	19.7	57
NL	0.999	100	100	9.3	27
FL	0.804	75	75	16.2	47
PL	0.417	100	50	7.9	23
CT 0.25	-	83.33	92.59	-	-
CT 0.5	-	91.6	81.48	-	-
CT 0.75	-	95.0	70.37	-	-

CL, corrugator lines; OorL, orbicularis oris lines; ML, mentalis lines; OocL, orbicularis oculi lines; NL, nasalis lines; FL, frontalis lines; PL, procerus lines; CT, confidence threshold; -, not available..

Figure 3. Overall accuracy of the model, confidence threshold, precision, recall, and area under the precision–recall curve (AUPRC). (A) Corrugator lines; (B) orbicularis oris lines; (C) mentalis lines; (D) orbicularis oculi lines; (E) nasalis lines; (F) frontalis lines; (G) procerus lines; (H) average AUPRC.

Accurate diagnosis and understanding of the likelihood of an illness or changes in the physical body rely on data collection and analysis. Healthcare providers learn from past diagnostic patterns and train on real cases in supervised clinical settings to obtain accurate results. In AI, machine learning uses artificial neural networks, which resemble biological neural networks, as computational models. These networks reveal complex structures and patterns in large datasets, such as medical images [13]. These networks can be fine-tuned and evolve based on experience. Consequently, they can be used as practical tools for pattern recognition, classification, and prediction.

Ophthalmologists, dermatologists, and radiologists use AI-AutoML algorithms to perform clinical classification tasks at a level comparable to that of traditional learning models for detecting diabetic retinopathy, skin cancer, and pneumonia [14-17]. AI-AutoML has proven to be a valuable tool for plastic surgeons; it aids in preoperative and postoperative decision-making processes, determines burn depth, and estimates healing time with an impressive accuracy rate of 86% [18]. This technology is particularly well suited for oculoplastic, aesthetic medicine, and plastic surgery research with an abundance of unstructured (unlabeled) visual data from easily accessible technologies [19]. A neural network developed using a sizable dataset of 18,000 images before and after rhinoplasty, exhibited an accuracy rate of 85% in classifying the rhinoplasty status of the tested photos, matching the sensitivity and specificity levels of ear, nose, and throat and plastic surgery professional, including residents and attendees [19]. The findings of this study demonstrate the effectiveness of a code-free AI-AutoML learning model as a reliable diagnostic tool for evaluating facial images and determining the need for BoNT/A neuromodulator injections for facial rejuvenation. The model achieved an impressive AUPRC value of 0.943.

Furthermore, with a confidence threshold of 0.5, the model exhibited a precision rate of 91.7% and a recall rate of 81.5%. These results emphasize the importance of AI applications and their potential to positively impact the quality of life and medical care of patients in specific clinical areas, particularly in procedures such as smoothing hyperkinetic lines and shaping the face through minimally invasive aesthetic procedures, such as injecting BoNT/A neuromodulators for facial rejuvenation. It is important to emphasize that the objective of this technology is to optimize the patient–physician relationship rather than replacing it.

Study limitations, future research, and ethics

This study has certain limitations that must be acknowledged. First, a limited number of images were available for the training, validation, and testing of our AI-AutoML model because of the significant cost associated with private funding of the Google Vertex AI public platform. In addition, a similar open-source and prospectively collected validation database was not available for comparison. Furthermore, initial photograding was performed by the same ophthalmologist who developed the model, which may have introduced some bias.

It is important to highlight that AI is a rapidly evolving technology lacking standardized ethical regulations. Numerous ethical considerations must be addressed to ensure the correct implementation of AI, including machine training ethics, data ownership, data protection, AI cybersecurity, machine accuracy, data transparency, image and research biases, and patient-related ethics. Unfortunately, in many cases, gold standards for these areas are not yet available. The focus of the ongoing research is to address these ethical questions and provide further insights into the implementation of AI in ophthalmology and medicine.

Alphabet Inc; stock owner.