Machine learning methods for determining skin age: A systematic review.

Journal: Journal of tissue viability
Published Date: Mar 22, 2025

Abstract

AIM: This systematic review explores how machine learning is used in determining skin aging, aiming to evaluate accuracy, limitations, and gaps in the current literature.

Authors

  • Eric McMullen
    Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
  • Rokhshid Aflaki
    Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
  • Pranav Jignesh Khatri
    Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
  • Dea Metko
    Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
  • Kyle Storm
    School of Health, University of Waterloo, Waterloo, Ontario, Canada.
  • Abu Bakar Butt
    Schulich School of Medicine, University of Western Ontario, London, Canada.
  • Mahan Maazi
    Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
  • Raghav Gupta
    Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. hshafiee@bwh.harvard.edu.
  • Rajan Grewal
    Division of Dermatology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
  • Trevor Champagne
    Dermatology Division, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Women's College Research Institute, Women's College Hospital, Toronto, ON, Canada.

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