Artificial intelligence for objectively measuring years regained after facial rejuvenation surgery

Elliott, Zachary; Bheemreddy, Alekya; Fiorella, Michele; Martin, Ann M.; Christopher, Vanessa; Krein, Howard; Heffelfinger, Ryan

doi:10.1016/j.amjoto.2022.103775

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…38,39 Finally, AI has been tested as an objective assessment tool for surgical outcomes and technique, which may have potential to evaluate resident performance in a less biased manner. [40][41][42]…”

Section: Utilization Of Ai Chatbots In Plastic Surgery Educationmentioning

confidence: 99%

Unveiling the Potential of AI in Plastic Surgery Education: A Comparative Study of Leading AI Platforms’ Performance on In-training Examinations

DiDonna,

Shetty,

Khan

et al. 2024

Plastic and Reconstructive Surgery - Global Open

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Background: Within the last few years, artificial intelligence (AI) chatbots have sparked fascination for their potential as an educational tool. Although it has been documented that one such chatbot, ChatGPT, is capable of performing at a moderate level on plastic surgery examinations and has the capacity to become a beneficial educational tool, the potential of other chatbots remains unexplored. Methods: To investigate the efficacy of AI chatbots in plastic surgery education, performance on the 2019–2023 Plastic Surgery In-service Training Examination (PSITE) was compared among seven popular AI platforms: ChatGPT-3.5, ChatGPT-4.0, Google Bard, Google PaLM, Microsoft Bing AI, Claude, and My AI by Snapchat. Answers were evaluated for accuracy and incorrect responses were characterized by question category and error type. Results: ChatGPT-4.0 outperformed the other platforms, reaching accuracy rates up to 79%. On the 2023 PSITE, ChatGPT-4.0 ranked in the 95th percentile of first-year residents; however, relative performance worsened when compared with upper-level residents, with the platform ranking in the 12th percentile of sixth-year residents. The performance among other chatbots was comparable, with their average PSITE score (2019–2023) ranging from 48.6% to 57.0%. Conclusions: Results of our study indicate that ChatGPT-4.0 has potential as an educational tool in the field of plastic surgery; however, given their poor performance on the PSITE, the use of other chatbots should be cautioned against at this time. To our knowledge, this is the first article comparing the performance of multiple AI chatbots within the realm of plastic surgery education.

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Section: Utilization Of Ai Chatbots In Plastic Surgery Educationmentioning

confidence: 99%

Unveiling the Potential of AI in Plastic Surgery Education: A Comparative Study of Leading AI Platforms’ Performance on In-training Examinations

DiDonna,

Shetty,

Khan

et al. 2024

Plastic and Reconstructive Surgery - Global Open

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“…After blepharoplasty, patient age was predicted to be 2.52 years younger than true chronological age [10]. A similar study conducted by Elliott et al [12] used CNN to predict patient age after deep plane facelift. The model was able to accurately predict preoperative age.…”

Section: Postoperative Aesthetic Outcomesmentioning

confidence: 99%

“…Artificial intelligence has shown promise in preoperative surgical planning, cross-sectional imaging analysis, facial palsy assessment, and developing inclusive sex, race, and aging evaluation tools [4 when benchmarking outcomes of FPRS procedures [10][11][12][13][14][15]. In a nonclinical setting, artificial intelligence has been used as a tool to drive research, supplement medical education, and provide digestible insights into care for patients [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Applications of artificial intelligence in facial plastic and reconstructive surgery: a systematic review

Souza,

Bhethanabotla,

Mohan

2024

Current Opinion in Otolaryngology &Amp; Head &Amp; Neck Surgery

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Purpose of review Arguably one of the most disruptive innovations in medicine of the past decade, artificial intelligence is dramatically changing how healthcare is practiced today. A systematic review of the most recent artificial intelligence advances in facial plastic surgery is presented for surgeons to stay abreast of the latest in our field. Recent findings Artificial intelligence applications developed for use in perioperative patient evaluation and management, education, and research in facial plastic surgery are highlighted. Selected themes include automated facial analysis with landmark detection, automated facial palsy grading and emotional assessment, generation of artificial facial profiles for testing and model training, automated postoperative patient communications, and improving ethnicity-sensitive facial morphometry norms. Inherent bias can exist in artificial intelligence models, and care must be taken to utilize algorithms trained with diverse datasets. Summary Artificial intelligence tools are helping clinicians provide more standardized, objective, and efficient care to their patients. Increasing surgeon awareness of available tools, and their widespread implementation into clinical workflows are the next frontier. Ethical considerations must also shape the adoption of any artificial intelligence functionality. As artificial intelligence applications become a fixture in medicine, surgeons must employ them effectively to stay at the vanguard of modern medicine.

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“…To objectively evaluate the effectiveness of FFS, previous studies have turned to artificial intelligence (AI), particularly facial recognition neural networks, to evaluate the perceived gender of patients before and after surgery 5 . Artificial intelligence has also been used to estimate the reduction in age appearance resulting from cosmetic surgeries such as face lifts 10–12 . The advantages of neural networks include their ability to be trained on vast amounts of data and their capacity for self-learning, which can help reduce bias 13 .…”

mentioning

confidence: 99%

“…5 Artificial intelligence has also been used to estimate the reduction in age appearance resulting from cosmetic surgeries such as face lifts. [10][11][12] The advantages of neural networks include their ability to be trained on vast amounts of data and their capacity for self-learning, which can help reduce bias. 13 However, to date, there have been no studies using AI to analyze changes in perceived age resulting from FFS nor has the relationship between change in gender appearance, age appearance, and patient satisfaction been investigated.…”

mentioning

confidence: 99%

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

Alper,

Almeida,

Hosseini

et al. 2023

Journal of Craniofacial Surgery

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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.

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Artificial intelligence for objectively measuring years regained after facial rejuvenation surgery

Cited by 9 publications

References 11 publications

Unveiling the Potential of AI in Plastic Surgery Education: A Comparative Study of Leading AI Platforms’ Performance on In-training Examinations

Unveiling the Potential of AI in Plastic Surgery Education: A Comparative Study of Leading AI Platforms’ Performance on In-training Examinations

Applications of artificial intelligence in facial plastic and reconstructive surgery: a systematic review

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

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