Applied Deep Learning in Plastic Surgery: Classifying Rhinoplasty With a Mobile App.

Journal: The Journal of craniofacial surgery
Published Date:

Abstract

BACKGROUND: Advances in deep learning (DL) have been transformative in computer vision and natural language processing, as well as in healthcare. The authors present a novel application of DL to plastic surgery. Here, the authors describe and demonstrate the mobile deployment of a deep neural network that predicts rhinoplasty status, assess model accuracy compared to surgeons, and describe future directions for such applications in plastic surgery.

Authors

  • Emily Borsting
    Department of Plastic Surgery, University of California, Irvine, CA.
  • Robert Desimone
    McGovern Institute for Brain Research, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, Massachusetts 02139, USA.
  • Mustafa Ascha
    Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine.
  • Mona Ascha
    Division of Plastic and Reconstructive Surgery, Department of Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH.