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

Journal: Aesthetic plastic surgery
PMID: 39187587

Abstract

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.

Authors

  • Jess D Rames
    Division of Plastic Surgery, Mayo Clinic, Rochester, Minnesota, USA.
  • Sara M Hussein
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, USA.
  • Abdallah A Shehab
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, USA.
  • Alexandre M Pazelli
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, USA.
  • Victoria A Sears
    Anatomic Modeling Lab, Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • Adam J Wentworth
    Anatomic Modeling Lab, Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • Jonathan M Morris
    Anatomic Modeling Lab, Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • Basel A Sharaf
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, USA. Sharaf.Basel@mayo.edu.

Keywords

External Resources

Popular Topics
Recent Journals