Artificial Intelligence in Surgical Evaluation: A Study of Facial Rejuvenation Techniques
Abstract Background Aesthetic facial surgeries historically rely on subjective analysis in determining success; this limits objective comparison of surgical outcomes. Objectives This case study exemplifies the use of an artificial intelligence software on objectively analyzing facial rejuvenation te...
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Published in: | Aesthetic surgery journal. Open forum Vol. 5; p. ojad032 |
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Main Authors: | , , , , |
Format: | Journal Article |
Language: | English |
Published: |
US
Oxford University Press
11-01-2023
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Subjects: | |
Online Access: | Get full text |
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Summary: | Abstract
Background
Aesthetic facial surgeries historically rely on subjective analysis in determining success; this limits objective comparison of surgical outcomes.
Objectives
This case study exemplifies the use of an artificial intelligence software on objectively analyzing facial rejuvenation techniques with the aim of reducing subjective bias.
Methods
Retrospectively, all patients who underwent facial rejuvenation surgery with concomitant procedures from 2015 to 2017 were included (n = 32). Patients were categorized into Groups A to C: Group A—10 superficial musculoaponeurotic system (SMAS) plication facelift (n = 10), Group B—SMASectomy facelift (n = 7), and Group C—high SMAS facelift (n = 15). Neutral repose images preoperatively and postoperatively (average >3 months) were analyzed using artificial intelligence for emotion and action unit alterations.
Results
Postoperatively, Group A experienced a decrease in happiness by 0.84% and a decrease in anger by 6.87% (P >> .1). Group B had an increase in happiness by 0.77% and an increase in anger by 1.91% (P >> .1). Both Group A and Group B did not show any discernable action unit patterns. In Group C, the lip corner puller AU increased in average intensity from 0% to 18.7%. This correlated with an average increase in detected happiness from 1.03% to 13.17% (P = .008). Conversely, the average detected anger decreased from 14.66% to 0.63% (P = .032).
Conclusions
This study provides the first proof of concept for the use of a machine learning software application to objectively assess various aesthetic surgical outcomes in facial rejuvenation. Due to limitations in patient heterogeneity, this study does not claim one technique's superiority but serves as a conceptual foundation for future investigation.
Level of Evidence: 4 |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Presented at: The Aesthetic Meeting in April 2022, San Diego, CA, USA. Dr Hebel is a medical student, Mayo Clinic Alix School of Medicine, Rochester, MN, USA. Dr Boonipat is the plastic surgery chief resident, Division of Plastic Surgery, Mayo Clinic, Rochester, MN, USA. Dr Bite is a plastic surgeon, Division of Plastic Surgery, Mayo Clinic, Rochester, MN, USA. Dr Lin is a plastic surgery resident, Division of Plastic Surgery, St. Louis University, St. Louis, MI, USA. Dr Shapiro is the director of resident aesthetic surgery training, Division of Plastic Surgery, Mayo Clinic, Rochester, MN, USA. |
ISSN: | 2631-4797 2631-4797 |
DOI: | 10.1093/asjof/ojad032 |