The applications of machine learning in plastic and reconstructive surgery: protocol of a systematic review

Using Generative Artificial Intelligence Tools in Cosmetic Surgery: A Study on Rhinoplasty, Facelifts, and Blepharoplasty Procedures

Artificial intelligence (AI), notably Generative Adversarial Networks, has the potential to transform medical and patient education. Leveraging GANs in medical fields, especially cosmetic surgery, provides a plethora of benefits, including upholding patient confidentiality, ensuring broad exposure to diverse patient scenarios, and democratizing medical education. This study investigated the capacity of AI models, DALL-E 2, Midjourney, and Blue Willow, to generate realistic images pertinent to cosmetic surgery. We combined the generative powers of ChatGPT-4 and Google's BARD with these GANs to produce images of various noses, faces, and eyelids. Four board-certified plastic surgeons evaluated the generated images, eliminating the need for real patient photographs. Notably, generated images predominantly showcased female faces with lighter skin tones, lacking representation of males, older women, and those with a body mass index above 20. The integration of AI in cosmetic surgery offers enhanced patient education and training but demands careful and ethical incorporation to ensure comprehensive representation and uphold medical standards.

Understanding the Impact of Aging on Attractiveness Using a Machine Learning Model of Facial Age Progression

Background: Advances in machine learning age progression technology offer the unique opportunity to better understand the public's perception on the aging face. Objective: To compare how observers perceive attractiveness and traditional gender traits in faces created with a machine learning model. Methods: Eight surveys were developed, each with 10 sets of photographs that were progressively aged with a machine learning model. Respondents rated attractiveness and masculinity or femininity of each photograph using a sliding scale (range: 0-100). Mean attractiveness scores were calculated and compared between men and women as well as between age groups. Results: A total of 315 respondents (51% men, 49% women) completed the survey. Accuracy of the facial age progression model was 85%. Females were considered significantly less attractive (-10.43, p < 0.01) and less feminine (-7.59, p < 0.01) per decade with the greatest drop over age 40 years. Male attractiveness and masculinity were relatively preserved until age 50 years where attractiveness scores were significantly lower (-5.45, p = 0.39). Conclusions: In this study, observers were found to perceive attractiveness at older ages differently between men and women.

Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery

INTRODUCTION

Machine learning (ML) is a set of models and methods that can detect patterns in vast amounts of data and use this information to perform various kinds of decision-making under uncertain conditions. This review explores the current role of this technology in plastic surgery by outlining the applications in clinical practice, diagnostic and prognostic accuracies, and proposed future direction for clinical applications and research.

METHODS

EMBASE, MEDLINE, CENTRAL and ClinicalTrials.gov were searched from 1990 to 2020. Any clinical studies (including case reports) which present the diagnostic and prognostic accuracies of machine learning models in the clinical setting of plastic surgery were included. Data collected were clinical indication, model utilised, reported accuracies, and comparison with clinical evaluation.

RESULTS

The database identified 1181 articles, of which 51 articles were included in this review. The clinical utility of these algorithms was to assist clinicians in diagnosis prediction (n=22), outcome prediction (n=21) and pre-operative planning (n=8). The mean accuracy is 88.80%, 86.11% and 80.28% respectively. The most commonly used models were neural networks (n=31), support vector machines (n=13), decision trees/random forests (n=10) and logistic regression (n=9).

CONCLUSIONS

ML has demonstrated high accuracies in diagnosis and prognostication of burn patients, congenital or acquired facial deformities, and in cosmetic surgery. There are no studies comparing ML to clinician's performance. Future research can be enhanced using larger datasets or utilising data augmentation, employing novel deep learning models, and applying these to other subspecialties of plastic surgery.

Validating machine learning approaches for prediction of donor related complication in microsurgical breast reconstruction: a retrospective cohort study

Autologous reconstruction using abdominal flaps remains the most popular method for breast reconstruction worldwide. We aimed to evaluate a prediction model using machine-learning methods and to determine which factors increase abdominal flap donor site complications with logistic regression. We evaluated the predictive ability of different machine learning packages, reviewing a cohort of breast reconstruction patients who underwent abdominal flaps. We analyzed 13 treatment variables for effects on the abdominal donor site complication rates. To overcome data imbalances, random over sampling example (ROSE) method was used. Data were divided into training and testing sets. Prediction accuracy, sensitivity, specificity, and predictive power (AUC) were measured by applying neuralnet, nnet, and RSNNS machine learning packages. A total of 568 patients were analyzed. The supervised learning package that performed the most effective prediction was neuralnet. Factors that significantly affected donor-related complication was size of the fascial defect, history of diabetes, muscle sparing type, and presence or absence of adjuvant chemotherapy. The risk cutoff value for fascial defect was 37.5 cm². High-risk group complication rates analyzed by statistical method were significant compared to the low-risk group (26% vs 1.7%). These results may help surgeons to achieve better surgical outcomes and reduce postoperative burden.