A Comprehensive Assessment of Soft-tissue Sagging after Zygoma Reduction Surgery through Artificial Intelligence Analysis.


Journal

Plastic and reconstructive surgery. Global open
ISSN: 2169-7574
Titre abrégé: Plast Reconstr Surg Glob Open
Pays: United States
ID NLM: 101622231

Informations de publication

Date de publication:
Aug 2024
Historique:
received: 04 03 2024
accepted: 21 06 2024
medline: 14 8 2024
pubmed: 14 8 2024
entrez: 14 8 2024
Statut: epublish

Résumé

Overdevelopment of zygomatic bones often results in protrusion and flaring of the midfacial region. This makes the face appear squarer than the more favorable oval shape. Therefore, zygoma reduction surgery has become a commonly performed procedure in patients seeking to obtain an ideal facial shape. Facial soft-tissue ptosis is one of the main complications of zygoma reduction surgery. Previously, the evaluation of cheek soft-tissue ptosis was subjectively based on patients and surgeons. Our study aimed to provide an objective evaluation of soft-tissue sagging in the cheek region after zygoma reduction surgery using artificial intelligence (AI). We used AI to evaluate cheek sagging in a series of patients who underwent zygoma reduction surgery. We used four methods: tracking facial landmarks, detecting changes in the cheek curvature, and examining changes in the nasolabial fold and marionette lines. Then, the obtained numerical results were assessed for statistically significant differences using statistical validation methods. Use of AI with the four methods demonstrated no statistically significant differences between the pre- and postsurgery evaluations. AI analysis demonstrated that soft-tissue ptosis did not occur in our series of patients. AI offers objective evaluation for both patients and doctors. Future research could build on this application to examine various influencing factors and develop new tools using machine learning to evaluate and predict the extent of cheek sagging in patients before surgery.

Sections du résumé

Background UNASSIGNED
Overdevelopment of zygomatic bones often results in protrusion and flaring of the midfacial region. This makes the face appear squarer than the more favorable oval shape. Therefore, zygoma reduction surgery has become a commonly performed procedure in patients seeking to obtain an ideal facial shape. Facial soft-tissue ptosis is one of the main complications of zygoma reduction surgery. Previously, the evaluation of cheek soft-tissue ptosis was subjectively based on patients and surgeons. Our study aimed to provide an objective evaluation of soft-tissue sagging in the cheek region after zygoma reduction surgery using artificial intelligence (AI).
Methods UNASSIGNED
We used AI to evaluate cheek sagging in a series of patients who underwent zygoma reduction surgery. We used four methods: tracking facial landmarks, detecting changes in the cheek curvature, and examining changes in the nasolabial fold and marionette lines. Then, the obtained numerical results were assessed for statistically significant differences using statistical validation methods.
Results UNASSIGNED
Use of AI with the four methods demonstrated no statistically significant differences between the pre- and postsurgery evaluations. AI analysis demonstrated that soft-tissue ptosis did not occur in our series of patients.
Conclusions UNASSIGNED
AI offers objective evaluation for both patients and doctors. Future research could build on this application to examine various influencing factors and develop new tools using machine learning to evaluate and predict the extent of cheek sagging in patients before surgery.

Identifiants

pubmed: 39139838
doi: 10.1097/GOX.0000000000006055
pii: GOX-D-24-00241
pmc: PMC11321748
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e6055

Informations de copyright

Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The American Society of Plastic Surgeons.

Déclaration de conflit d'intérêts

The authors have no financial interest to declare in relation to the content of this article.

Auteurs

Yun Yong Park (YY)

From the Department of Plastic and Reconstructive Surgery, iWELL Plastic Surgery Clinic, Seoul, South Korea.

Kenneth K Kim (KK)

Division of Plastic and Reconstructive Surgery, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, Calif.
Department of Plastic and Reconstructive Surgery, Dream Medical Group, Los Angeles, Calif.
Department of Plastic and Reconstructive Surgery, Seoul National University College of Medicine, Seoul, South Korea.

Bumjin Park (B)

From the Department of Plastic and Reconstructive Surgery, iWELL Plastic Surgery Clinic, Seoul, South Korea.

Classifications MeSH