Facelift Surgery Turns Back the Clock: Artificial Intelligence and Patient Satisfaction Quantitate Value of Procedure Type and Specific Techniques

Artificial Intelligence in Facial Plastic and Reconstructive Surgery: A Systematic Review

Artificial intelligence (AI) is a technology that is evolving rapidly and is changing the world and medicine as we know it. After reviewing the PROSPERO database of systematic reviews, there is no article related to this topic in facial plastic and reconstructive surgery. The objective of this article was to review the literature regarding AI applications in facial plastic and reconstructive surgery.A systematic review of the literature about AI in facial plastic and reconstructive surgery using the following keywords: Artificial Intelligence, robotics, plastic surgery procedures, and surgery plastic and the following databases: PubMed, SCOPUS, Embase, BVS, and LILACS. The inclusion criteria were articles about AI in facial plastic and reconstructive surgery. Articles written in a language other than English and Spanish were excluded. In total, 17 articles about AI in facial plastic met the inclusion criteria; after eliminating the duplicated papers and applying the exclusion criteria, these articles were reviewed thoroughly. The leading type of AI used in these articles was computer vision, explicitly using models of convolutional neural networks to objectively compare the preoperative with the postoperative state in multiple interventions such as facial lifting and facial transgender surgery.In conclusion, AI is a rapidly evolving technology, and it could significantly impact the treatment of patients in facial plastic and reconstructive surgery. Legislation and regulations are developing slower than this technology. It is imperative to learn about this topic as soon as possible and that all stakeholders proactively promote discussions about ethical and regulatory dilemmas.

Artificial Intelligence in Facial Plastics and Reconstructive Surgery

Artificial intelligence (AI), particularly computer vision and large language models, will impact facial plastic and reconstructive surgery (FPRS) by enhancing diagnostic accuracy, refining surgical planning, and improving post-operative evaluations. These advancements can address subjective limitations of aesthetic surgery by providing objective tools for patient evaluation. Despite these advancements, AI in FPRS has yet to be fully integrated in the clinic setting and faces numerous challenges including algorithmic bias, ethical considerations, and need for validation. This article discusses current and emerging AI technologies in FPRS for the clinic setting, providing a glimpse of its future potential.

A Systematic Review of Questionnaires Assessing Patient Satisfaction in Plastic Surgery: Tools, Topics, and Surgical Types

Background

Patient satisfaction is crucial for evaluating healthcare services, including plastic surgery. This systematic review aims to analyze questionnaires assessing patient satisfaction in plastic surgery, identifying their strengths and weaknesses to improve outcomes and enhance the quality of care.

Methods

A comprehensive literature search was conducted using electronic databases. Studies were included if they were original research articles, written in English, and focused on patient satisfaction questionnaires in plastic surgery. Data extraction and descriptive statistics were used to summarize the data.

Results

A total of 105 studies were included. General/overall satisfaction was the most common topic addressed (99.04%). Cosmetic outcomes were the most frequently assessed category (34.3%). Breast reconstruction was the most common procedure (33.3%). Most studies used a combination of generic and procedure-specific questionnaires (45.71%). The most frequently used measurement tools were BREAST-Q and self-developed questionnaires, each accounting for 28.57% and 27.61%.

Conclusions

This review provides a comprehensive analysis of patient satisfaction questionnaires in plastic surgery, emphasizing the importance of a holistic approach and well-established, validated tools. The findings contribute to improving plastic surgery outcomes and enhancing the quality of care. Future research should refine assessment tools to address patients' needs and promote patient-centered outcomes in plastic surgery.

Enhancing Patient Safety in Prehospital Environment: Analyzing Patient Perspectives on Non-Transport Decisions With Natural Language Processing and Machine Learning

OBJECTIVE

This research explored the experiences and perspectives of patients declining hospital transportation after receiving prehospital emergency care using advanced computational techniques.

METHOD

Between 15th June and 1st August 2023, 210 patients in Qatar, treated by Hamad Medical Corporation Ambulance Service (HMCAS) but refusing transportation to hospital, were interviewed. Key outcome variables stratified by demographics included "reasons for refusing transport," "satisfaction with HMCAS service," and "postrefusal actions." Responses underwent sentiment analysis and topic modeling using latent Dirichlet allocation. Machine learning models, such as Naïve Bayes, K-nearest neighboring, random forest, and support vector machine, were used to predict patients' subsequent actions.

RESULTS

Participants had an average age of 38.61 ± 19.91 years. The chief complaints were primarily chest and abdominal pains (18.49%; n = 39). Sentiment Analysis revealed a generally favorable perception of HMCAS-provided service. Latent Dirichlet allocation identified two main topics pertaining to refusal reasons and service satisfaction. Naïve Bayes and support vector machine algorithms were most effective in predicting postrefusal actions with an accuracy rate of 81.58%.

CONCLUSIONS

This study highlighted the utility of Natural Language Processing and ML in enhancing our understanding of patient behaviors and sentiments in prehospital settings. These advanced computational methodologies allowed for a nuanced exploration of patient demographics and sentiments, providing insights for Quality Improvement initiatives. The study also advocates for continuously integrating automated feedback mechanisms to improve patient-centered care in the prehospital context. Continuous integration of automated feedback systems is recommended to improve prehospital patient-centered care.

Age Reduction After Face-Lift Surgery in Chinese Population: An Outcome Study Using Artificial Intelligence and Objective Observer-Based Assessment

BACKGROUND

The literature is replete with favorable face-lift results, yet the objective facial rejuvenation outcome measures in Chinese women have remained poorly understood.

OBJECTIVE

The purpose of the study is to objectively evaluate the apparent age (AA) reduction in Chinese women following face-lift by artificial intelligence (AI) and objective observers.

METHODS

Standardized pre- and postoperative (1-year) images of 48 patients undergoing face-lift procedures were analyzed by AI to estimate AA. Additionally, 10 blinded, naive observers viewed each patient's images and assessed AA. The accuracy of AA and reduction in AA were evaluated and compared between the two methods. FACE-Q surveys were employed to measure patient-reported facial esthetic outcomes.

RESULTS

The AI demonstrated higher precision than the observers in age estimation, with a mean absolute error of 3.34 years and 90% Pearson correlation. AA reduction generated by AI was significantly lower than that by observers, with a mean reduction of 3.75 ± 3.93 and 4.51 ± 1.20, respectively (p < 0.05). However, both methods showed less AA reduction than patient self-appraisal (- 7.3 years). Improvements in facial rejuvenation following face-lift surgery is relevant to the patient's preoperative aging status. Patients whose pre-AA was greater than chronological age (CA) became "back to normal," while those whose pre-AA was less than CA became "turning back the clock."

CONCLUSION

The utilization of AI could provide objective, evidence-based data in the field of face-lift surgery. As a simple, complete, and time-sparing method, AI is expected to be routinely used in clinical trials and practice.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Deep learning and explainable artificial intelligence for investigating dental professionals' satisfaction with CAD software performance

PURPOSE

This study aimed to examine the satisfaction of dental professionals, including dental students, dentists, and dental technicians, with computer-aided design (CAD) software performance using deep learning (DL) and explainable artificial intelligence (XAI)-based behavioral analysis concepts.

MATERIALS AND METHODS

This study involved 436 dental professionals with diverse CAD experiences to assess their satisfaction with various dental CAD software programs. Through exploratory factor analysis, latent factors affecting user satisfaction were extracted from the observed variables. A multilayer perceptron artificial neural network (MLP-ANN) model was developed along with permutation feature importance analysis (PFIA) and the Shapley additive explanation (Shapley) method to gain XAI-based insights into individual factors' significance and contributions.

RESULTS

The MLP-ANN model outperformed a standard logistic linear regression model, demonstrating high accuracy (95%), precision (84%), and recall rates (84%) in capturing complex psychological problems related to human attitudes. PFIA revealed that design adjustability was the most important factor impacting dental CAD software users' satisfaction. XAI analysis highlighted the positive impacts of features supporting the finish line and crown design, while the number of design steps and installation time had negative impacts. Notably, finish-line design-related features and the number of design steps emerged as the most significant factors.

CONCLUSIONS

This study sheds light on the factors influencing dental professionals' decisions in using and selecting CAD software. This approach can serve as a proof-of-concept for applying DL-XAI-based behavioral analysis in dentistry and medicine, facilitating informed software selection and development.

Surgical Procedures of the Correction of Severe Static Glabellar Lines by Utilizing Resection with Free Dermal Fat Grafting (FDFG)

BACKGROUND

Botulinum toxin alone is unable to sufficiently reduce the muscle in cases of severe static glabellar lines due to the folded skin and dermal breakdown that frequently accompany these conditions. Augmentation of the soft tissue and removal of folded skin at the same time is the final solution. To simultaneously resolve interbrow skin laxity and replenish tissue volume, we present for the first time the method of glabellar lines excision combined with FDFG.

METHODS

This retrospective study involved 23 patients with moderate-to-severe static glabellar lines underwent resection and/or free dermal fat grafting (FDFG) from June 2022 to June 2023. Fifteen of them underwent glabellar lines excision combined with FDFG, and seven were filled only. These patients were followed up at least 6 months to evaluate the effect. We utilized FACE-Q and WSRS for assessment in order to investigate the clinical results.

RESULTS

There is no complication such as discoloration, hematoma, infection and palpability in all cases. After 6-15 months of follow-up, all the patients' dynamic and static lines were improved to a certain degree, and the patients were satisfied with the results with the WSRS score decreased from 3.5 ± 0.47 to 1.8 ± 0.62, and FACE-Q assessments in "Line between the eyebrows" decreased from 87 ± 7.39 to 43 ± 10.3.

CONCLUSIONS

Resection in conjunction with FDFG is a brief, innovative and effective technique to correct static and dynamic severe glabellar wrinkles and maintain an acceptable outcome over an extended period of time which worthy clinical promotion.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Differences in the annotation between facial images and videos for training an artificial intelligence for skin type determination

BACKGROUND

The Grand-AID research project, consisting of GRANDEL-The Beautyness Company, the dermatology department of Augsburg University Hospital and the Chair of IT Infrastructure for Translational Medical Research at Augsburg University, is currently researching the development of a digital skin consultation tool that uses artificial intelligence (AI) to analyze the user's skin and ultimately perform a personalized skin analysis and a customized skin care routine. Training the AI requires annotation of various skin features on facial images. The central question is whether videos are better suited than static images for assessing dynamic parameters such as wrinkles and elasticity. For this purpose, a pilot study was carried out in which the annotations on images and videos were compared.

MATERIALS AND METHODS

Standardized image sequences as well as a video with facial expressions were taken from 25 healthy volunteers. Four raters with dermatological expertise annotated eight features (wrinkles, redness, shine, pores, pigmentation spots, dark circles, skin sagging, and blemished skin) with a semi-quantitative and a linear scale in a cross-over design to evaluate differences between the image modalities and between the raters.

RESULTS

In the videos, most parameters tended to be assessed with higher scores than in the images, and in some cases significantly. Furthermore, there were significant differences between the raters.

CONCLUSION

The present study shows significant differences between the two evaluation methods using image or video analysis. In addition, the evaluation of the skin analysis depends on subjective criteria. Therefore, when training the AI, we recommend regular training of the annotating individuals and cross-validation of the annotation.

Artificial Intelligence: Singularity Approaches

SUMMARY

Artificial intelligence (AI) has been a disruptive technology within health care, from the development of simple care algorithms to complex deep-learning models. AI has the potential to reduce the burden of administrative tasks, advance clinical decision-making, and improve patient outcomes. Unlocking the full potential of AI requires the analysis of vast quantities of clinical information. Although AI holds tremendous promise, widespread adoption within plastic surgery remains limited. Understanding the basics is essential for plastic surgeons to evaluate the potential uses of AI. This review provides an introduction of AI, including the history of AI, key concepts, applications of AI in plastic surgery, and future implications.

Perceived Age and Gender Perception Using Facial Recognition Software Following Facial Feminization Surgery

Measures of success for facial feminization surgery (FFS) have previously included improved rates of external gender perception as female and patient-reported outcome measures. In this study, we used artificial intelligence facial recognition software to objectively evaluate the effects of FFS on both perceived gender and age among male-to-female transgender patients, as well as their relationship with patient facial satisfaction. Standardized frontal preoperative and postoperative images of 27 transgender women undergoing FFS were analyzed by Amazon's AI facial recognition software to determine gender, femininity confidence score, and perceived age. Female gender-typing, improvement in gender-typing (preoperatively to postoperatively), and femininity confidence scores were analyzed. To assess patient satisfaction, FACE-Q modules were completed postoperatively. Preoperatively, FFS images were perceived as female 48.1% of the time, and postoperatively, this improved to 74.1% ( P =0.05). Femininity confidence scores improved from a mean score of 0.04 preoperatively to 0.39 postoperatively ( P =0.003). FFS was associated with a decrease in perceived age relative to the patient's true age (-2.4 y, P <0.001), with older patients experiencing greater reductions. Pearson correlation matrix found no significant relationship between improved female gender typing and patient facial satisfaction. Undergoing surgery at a younger age was associated with higher overall facial satisfaction ( r =-0.6, P =0.01). Transfeminine patients experienced improvements in satisfaction with facial appearance, perceived gender, and decreases in perceived age following FFS. Notably, patient satisfaction was not directly associated with improved AI-gender typing, suggesting that other factors may influence patient satisfaction.

How Brow Rotation Affects Emotional Expression Utilizing Artificial Intelligence

BACKGROUND

It is well known that brow position affects emotional expression. However, there is little literature on how and to what degree this change in emotional expression happens. Previous studies on this topic have utilized manual rating; this method of study remains small and labor intensive. Our objective is to correlate manual brow rotations with emotional outcomes using artificial intelligence to objectively determine how specific brow manipulations affected human expression.

METHODS

We included 53 brow-lift patients in this study. Pre-operative patients' brows were rotated to - 20, - 10, +10, and +20 degrees in respect to the central axis of their existing brow using PIXLR, a cloud-based set of image editing tools and utilities. These images were analyzed using FaceReader, a validated software package that uses computer vision technology for facial expression recognition. The primary facial emotion and intensity of facial action units (0 = no action unit detected to 4 = most intense action unit detected) generated by the software were recorded.

RESULTS

265 total images [5 images (pre-operative, - 20 degree brow rotation, - 10, +10, and +20) per patient] were analyzed using FaceReader. The primary emotion detected in the majority of images was neutral. The percentage of disgust in patients' expressions, as detected by FaceReader, increased with increased positive brow rotation (1.76% disgust detected at - 20 degrees, 2.09% at - 10 degrees, 2.65% at neutral, 2.61% at +10 degrees, and 2.95% at +20 degrees). In contrast, the percentage of sadness in patients' expressions decreased with increased positive brow rotation (29.92% sadness detected at - 20 degrees, 21.5% at - 10 degrees, 11.42% at neutral, 15.75% at +10 degrees, and 12.86% at +20 degrees). Our facial action unit analysis corresponded with primary emotion analysis. The intensity of the inner brow raiser decreased with increased positive brow rotation 8.54% at - 20 degrees, 4.21% at - 10 degrees, 1.48% at neutral, 0.84% at +10 degrees, and 0.76% at +20 degrees). The intensity of the outer brow raiser increased with increased positive brow rotation (0.97% at - 20 degrees, 0.45% at - 10 degrees, 1.12% at neutral, 5.45% at +10 degrees, and 11.19% at +20 degrees).

CONCLUSION

We demonstrated that increasing the degree of brow rotation correlated positively with the percentage of disgust and inversely with the percentage of sadness detected by FaceReader. This study demonstrated how different manipulated brow positions affected emotional outcomes using artificial intelligence. Physicians can use these findings to better understand how brow-lifts can affect the perceived emotion of their patients.

LEVEL OF EVIDENCE III

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

A Narrative Review of Artificial Intelligence (AI) for Objective Assessment of Aesthetic Endpoints in Plastic Surgery

Notoriously characterized by subjectivity and lack of solid scientific validation, reporting aesthetic outcome in plastic surgery is usually based on ill-defined end points and subjective measures very often from the patients' and/or providers' perspective. With the tremendous increase in demand for all types of aesthetic procedures, there is an urgent need for better understanding of aesthetics and beauty in addition to reliable and objective outcome measures to quantitate what is perceived as beautiful and attractive. In an era of evidence-based medicine, recognition of the importance of science with evidence-based approach to aesthetic surgery is long overdue. View the many limitations of conventional outcome evaluation tools of aesthetic interventions, objective outcome analysis provided by tools described to be reliable is being investigated such as advanced artificial intelligence (AI). The current review is intended to analyze available evidence regarding advantages as well as limitations of this technology in objectively documenting outcome of aesthetic interventions. It has shown that some AI applications such as facial emotions recognition systems are capable of objectively measuring and quantitating patients' reported outcomes and defining aesthetic interventions success from the patients' perspective. Though not reported yet, observers' satisfaction with the results and their appreciation of aesthetic attributes may also be measured in the same manner.Level of Evidence III This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Artificial Intelligence in Facial Plastic Surgery: A Review of Current Applications, Future Applications, and Ethical Considerations

From virtual chat assistants to self-driving cars, artificial intelligence (AI) is often heralded as the technology that has and will continue to transform this generation. Among widely adopted applications in other industries, its potential use in medicine is being increasingly explored, where the vast amounts of data present in electronic health records and need for continuous improvements in patient care and workflow efficiency present many opportunities for AI implementation. Indeed, AI has already demonstrated capabilities for assisting in tasks such as documentation, image classification, and surgical outcome prediction. More specifically, this technology can be harnessed in facial plastic surgery, where the unique characteristics of the field lends itself well to specific applications. AI is not without its limitations, however, and the further adoption of AI in medicine and facial plastic surgery must necessarily be accompanied by discussion on the ethical implications and proper usage of AI in healthcare. In this article, we review current and potential uses of AI in facial plastic surgery, as well as its ethical ramifications.

Best Practices for FACE-Q Aesthetics Research: A Systematic Review of Study Methodology

BACKGROUND

The FACE-Q Aesthetics module is a validated patient-reported outcome measure (PROM) that evaluates perspectives on facial aesthetic treatments. Improper administration and poor study methodology can compromise the validity and interpretation of this PROM.

OBJECTIVES

This systematic review sought to evaluate the administration and scoring of the FACE-Q Aesthetics scales within the literature.

METHODS

A search of Ovid Medline, Embase, Cochrane, and Web of Science was performed on December 20, 2022, with the assistance of a health-research librarian (CRD42022383676). Studies that examined facial aesthetic interventions using the FACE-Q Aesthetics module as a primary or secondary outcome measure were included for analysis.

RESULTS

There were 114 studies included. The Face Overall (n = 52, 45.6%), Psychological (n = 45, 39.4%), and Social (n = 43, 37.7%) scales were most frequently reported. Errors in FACE-Q administration were identified in 30 (26.3%) studies. The most common error was the presentation of raw ordinal scores rather than the converted Q score (n = 23). Most studies reported a time horizon for their primary analysis (n = 76, 66.7%); however, only 4 studies provided a rationale for this selection. Sample size calculations for the primary outcome were rarely performed (n = 9, 7.9%).

CONCLUSIONS

There continues to be limitations in PROM administration and the quality of articles that report FACE-Q Aesthetic scale data. The authors suggest that future investigators using the FACE-Q refer to the User's Guide regarding administration and scoring of this scale, report a rationale for the study time horizon, and provide an a priori sample size calculation for the primary outcome of interest.

Applications of Machine Learning in Facial Cosmetic Surgeries: A Scoping Review

OBJECTIVE

To review the application of machine learning (ML) in the facial cosmetic surgeries and procedures METHODS AND MATERIALS: Electronic search was conducted in PubMed, Scopus, Embase, Web of Science, ArXiv and Cochrane databases for the studies published until August 2022. Studies that reported the application of ML in various fields of facial cosmetic surgeries were included. The studies' risk of bias (ROB) was assessed using the QUADAS-2 tool and NIH tool for before and after studies.

RESULTS

From 848 studies, a total of 29 studies were included and categorized in five groups based on the aim of the studies: outcome evaluation (n = 8), face recognition (n = 7), outcome prediction (n = 7), patient concern evaluation (n = 4) and diagnosis (n = 3). Total of 16 studies used public data sets. ROB assessment using QUADAS-2 tool revealed that six studies were at low ROB, five studies were at high ROB, and others had moderate ROB. All studies assessed with NIH tool showed fair quality. In general, all studies showed that using ML in the facial cosmetic surgeries is accurate enough to benefit both surgeons and patients.

CONCLUSION

Using ML in the field of facial cosmetic surgery is a novel method and needs further studies, especially in the fields of diagnosis and treatment planning. Due to the small number of articles and the qualitative analysis conducted, we cannot draw a general conclusion about the impact of ML in the sphere of facial cosmetic surgery.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Evidence-Based Medicine for Lower Facial Rejuvenation

Lower facial rejuvenation is an expanding area in facial plastic surgery with both surgical and nonsurgical treatment options. Evidence-based medicine is essential to providing high-quality care and creating long-lasting results. A systematic approach and understanding of the layers of the aging lower face is important to develop an individualized treatment plan. This review will focus on surgical and nonsurgical treatments for the aging lower face with an emphasis on evidence-based medicine.

The Use of the FACE-Q Aesthetic: A Narrative Review

INTRODUCTION

In the past decade there has been an increasing interest in the field of patient-reported outcome measures (PROMs) which are now commonly used alongside traditional outcome measures, such as morbidity and mortality. Since the FACE-Q Aesthetic development in 2010, it has been widely used in clinical practice and research, measuring the quality of life and patient satisfaction. It quantifies the impact and change across different aspects of cosmetic facial surgery and minimally invasive treatments. We review how researchers have utilized the FACE-Q Aesthetic module to date, and aim to understand better whether and how it has enhanced our understanding and practice of aesthetic facial procedures.

METHODS

We performed a systematic search of the literature. Publications that used the FACE-Q Aesthetic module to evaluate patient outcomes were included. Publications about the development of PROMs or modifications of the FACE-Q Aesthetic, translation or validation studies of the FACE-Q Aesthetic scales, papers not published in English, reviews, comments/discussions, or letters to the editor were excluded.

RESULTS

Our search produced 1189 different articles; 70 remained after applying in- and exclusion criteria. Significant findings and associations were further explored. The need for evidence-based patient-reported outcome caused a growing uptake of the FACE-Q Aesthetic in cosmetic surgery and dermatology an increasing amount of evidence concerning facelift surgery, botulinum toxin, rhinoplasty, soft tissue fillers, scar treatments, and experimental areas.

DISCUSSION

The FACE-Q Aesthetic has been used to contribute substantial evidence about the outcome from the patient perspective in cosmetic facial surgery and minimally invasive treatments. The FACE-Q Aesthetic holds great potential to improve quality of care and may fundamentally change the way we measure success in plastic surgery and dermatology.

LEVEL OF EVIDENCE III

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Use of Micro Botulinum Toxin for a Face-lifting Effect: A Systematic Review

BACKGROUND

Micro-Botox (Micro-btx) was described in 2000 for the paralysis of superficial muscle fibers to address facial rhytides. Increasingly, there are reports of its off-label use for a face-lifting effect.

OBJECTIVE

To evaluate the literature for such results.

METHODS AND METHODS

A systematic review was performed according to PRISMA; only Level ≥ III evidence from 2000 to 2020 were included. Data extracted include patient demographics, type of botulinum toxin, dilution, dosage, injection sites and spacing, needle size and syringe, follow-up, patient and physician assessment, and complications.

RESULTS

Three hundred seventy-two patients (average 35.2 years) underwent different botulinum toxin injections (average 39 units/hemiface) of varying dilutions with 30- to 32-G needles, typically with 1-mL syringes, by forming 0.2- to 0.5-cm wheals 1 cm apart. Follow-up averaged 10.5 weeks with both subjective and objective assessments. Facial asymmetry and minor bruising were common. Subjective assessment of face-lifting effects between patients and physicians was highly discordant and injection sites reported were highly variable.

CONCLUSION

Much heterogeneity in dosage, injection sites, definition of "face-lifting," and assessment methods remain, all of which preclude accurate and objective evaluation of the current evidence for micro-btx. Future studies should address these variables, given the growing interest in such nonsurgical options for a face-lifting effect.

The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review

Interest in the use of artificial intelligence (AI) and machine learning (ML) in medicine has grown exponentially over the last few years. With its ability to enhance speed, precision, and efficiency, AI has immense potential, especially in the field of surgery. This article aims to provide a comprehensive literature review of artificial intelligence as it applies to surgery and discuss practical examples, current applications, and challenges to the adoption of this technology. Furthermore, we elaborate on the utility of natural language processing and computer vision in improving surgical outcomes, research, and patient care.