Combining Deep Learning With Optical Coherence Tomography Imaging to Determine Scalp Hair and Follicle Counts.

Journal: Lasers in surgery and medicine
PMID: 32960994

Abstract

BACKGROUND AND OBJECTIVES: One of the challenges in developing effective hair loss therapies is the lack of reliable methods to monitor treatment response or alopecia progression. In this study, we propose the use of optical coherence tomography (OCT) and automated deep learning to non-invasively evaluate hair and follicle counts that may be used to monitor the success of hair growth therapy more accurately and efficiently.

Authors

  • Gregor Urban
    Department of Computer Science, University of California, Irvine , Irvine, California 92697, United States.
  • Nate Feil
    Department of Dermatology, School of Medicine, University of California, Irvine, California, 92697.
  • Ella Csuka
    Department of Dermatology, School of Medicine, University of California, Irvine, California, 92697.
  • Kiana Hashemi
    Department of Dermatology, School of Medicine, University of California, Irvine, California, 92697.
  • Chloe Ekelem
    Department of Dermatology, School of Medicine, University of California, Irvine, California, 92697.
  • Franchesca Choi
    Department of Computer Science, University of California, Irvine, California, 92697.
  • Natasha A Mesinkovska
    Department of Dermatology, School of Medicine, University of California, Irvine, California, 92697.
  • Pierre Baldi
    Department of Computer Science, Department of Biological Chemistry, University of California-Irvine, Irvine, CA 92697, USA.

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