Mandibular Gender Dimorphism: The Utility of Artificial Intelligence and Statistical Shape Modeling in Skeletal Facial Analysis.

Gender affirmation surgery Gender dimorphism Mandibular contour Sexual dimorphism Statistical shape modeling

Journal

Aesthetic plastic surgery
ISSN: 1432-5241
Titre abrégé: Aesthetic Plast Surg
Pays: United States
ID NLM: 7701756

Informations de publication

Date de publication:
26 Aug 2024
Historique:
received: 24 06 2024
accepted: 01 08 2024
medline: 27 8 2024
pubmed: 27 8 2024
entrez: 26 8 2024
Statut: aheadofprint

Résumé

In gender-affirming surgery, facial skeletal dimorphism is an important topic for every craniofacial surgeon. Few cephalometric studies have assessed this topic; however, they fall short to provide skeletal contour insights that direct surgical planning. Herein, we propose statistical shape modeling (SSM) as a novel tool for investigating mandibular dimorphism for young white individuals. A single-center, retrospective study was performed using computed tomography (CT) scans of white individuals, aged 20 to 39 years old. AI-assisted, three-dimensional (3D) mandibles were reconstructed in Materialise Mimics v25.0. We used SSM to generate average 3D models for both genders. Relevant manual anthropometric measurements were taken for the SSMs and individual mandibles. Contour disparities were then represented using 3D overlays and heatmaps. Statistical analyses were performed using unpaired student t testing or Wilcoxon signed rank testing with 95% confidence interval as deemed appropriate by population-level normality assessment. Ninety-eight patients (53 females, 45 males) were included. Male mandibles showed greater bigonial width, intercondylar width, ramus height, and body length [p<0.005]. There was no statistically significant difference in the gonial angle measurements [p=0.62]. All relevant manual individual measurements demonstrated excellent concordance to their SSM counterparts. The 3D overlays of SSMs revealed squarer male chins with more lateral but less anterior projection than their female counterparts. Also, the female mandibles showed smoother transition at the gonial angle. SSM provides a novel tool to objectively evaluate volumetric and contour dimorphisms between genders. Moreover, this method can be automated, allowing for expedited comparisons between populations of interest compared to manual assessment. 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 . Bullet points about the importance of this work: Advancing Anthropometric Assessment: Statistical shape modeling (SSM) offers a cutting-edge approach to visualizing gender-specific skeletal anatomic differences for aesthetic and gender-affirming facial surgery. Expediting Comparative Analysis: The workflow established in this paper streamlines the evaluative process, enabling rapid morphologic comparisons between populations. Patient-Centered Care: This study establishes a foundation for the development of SSMs in individualized operative planning.

Sections du résumé

BACKGROUND BACKGROUND
In gender-affirming surgery, facial skeletal dimorphism is an important topic for every craniofacial surgeon. Few cephalometric studies have assessed this topic; however, they fall short to provide skeletal contour insights that direct surgical planning. Herein, we propose statistical shape modeling (SSM) as a novel tool for investigating mandibular dimorphism for young white individuals.
METHODS METHODS
A single-center, retrospective study was performed using computed tomography (CT) scans of white individuals, aged 20 to 39 years old. AI-assisted, three-dimensional (3D) mandibles were reconstructed in Materialise Mimics v25.0. We used SSM to generate average 3D models for both genders. Relevant manual anthropometric measurements were taken for the SSMs and individual mandibles. Contour disparities were then represented using 3D overlays and heatmaps. Statistical analyses were performed using unpaired student t testing or Wilcoxon signed rank testing with 95% confidence interval as deemed appropriate by population-level normality assessment.
RESULTS RESULTS
Ninety-eight patients (53 females, 45 males) were included. Male mandibles showed greater bigonial width, intercondylar width, ramus height, and body length [p<0.005]. There was no statistically significant difference in the gonial angle measurements [p=0.62]. All relevant manual individual measurements demonstrated excellent concordance to their SSM counterparts. The 3D overlays of SSMs revealed squarer male chins with more lateral but less anterior projection than their female counterparts. Also, the female mandibles showed smoother transition at the gonial angle.
CONCLUSIONS CONCLUSIONS
SSM provides a novel tool to objectively evaluate volumetric and contour dimorphisms between genders. Moreover, this method can be automated, allowing for expedited comparisons between populations of interest compared to manual assessment.
LEVEL OF EVIDENCE III METHODS
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 . Bullet points about the importance of this work: Advancing Anthropometric Assessment: Statistical shape modeling (SSM) offers a cutting-edge approach to visualizing gender-specific skeletal anatomic differences for aesthetic and gender-affirming facial surgery. Expediting Comparative Analysis: The workflow established in this paper streamlines the evaluative process, enabling rapid morphologic comparisons between populations. Patient-Centered Care: This study establishes a foundation for the development of SSMs in individualized operative planning.

Identifiants

pubmed: 39187587
doi: 10.1007/s00266-024-04300-x
pii: 10.1007/s00266-024-04300-x
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. Springer Science+Business Media, LLC, part of Springer Nature and International Society of Aesthetic Plastic Surgery.

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Auteurs

Jess D Rames (JD)

Division of Plastic and Reconstructive Surgery, Department of Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, USA.

Sara M Hussein (SM)

Division of Plastic and Reconstructive Surgery, Department of Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, USA.
Neural Engineering and Precision Surgery Laboratories (NEPS), Mayo Clinic, Rochester, MN, USA.

Abdallah A Shehab (AA)

Division of Plastic and Reconstructive Surgery, Department of Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, USA.

Alexandre M Pazelli (AM)

Division of Plastic and Reconstructive Surgery, Department of Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, USA.

Victoria A Sears (VA)

Anatomic Modeling Lab, Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Adam J Wentworth (AJ)

Anatomic Modeling Lab, Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Jonathan M Morris (JM)

Anatomic Modeling Lab, Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Division of Neuroradiology, Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Basel A Sharaf (BA)

Division of Plastic and Reconstructive Surgery, Department of Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, USA. Sharaf.Basel@mayo.edu.
Neural Engineering and Precision Surgery Laboratories (NEPS), Mayo Clinic, Rochester, MN, USA. Sharaf.Basel@mayo.edu.
Center for Aesthetic Medicine and Surgery, Mayo Clinic, Rochester, MN, USA. Sharaf.Basel@mayo.edu.

Classifications MeSH