Development and qualification of a machine learning algorithm for automated hair counting.

Journal: International journal of cosmetic science
PMID: 34426987

Abstract

OBJECTIVE: Determining the amount of hair on the scalp has always been an important metric of patient satisfaction for hair growth and hair retention technologies. While simple in concept, this measurement is a difficult, resource intensive task for the dermatologist and the research scientist. Specifically, counting and measuring hair in phototrichogram images is very time consuming and labour intensive. Due to cost, often only a fraction of available images is manually analysed. There is a need for an automated method that can significantly increase speed and throughput while reducing the cost of counting and measuring hair in phototrichogram images.

Authors

  • Jarek P Sacha
    The Procter and Gamble Company, Mason, Ohio, USA.
  • Tamara L Caterino
    The Procter and Gamble Company, Mason, Ohio, USA.
  • Brian K Fisher
    The Procter and Gamble Company, Mason, Ohio, USA.
  • Gregory J Carr
    The Procter and Gamble Company, Mason, Ohio, USA.
  • R Scott Youngquist
    Former P&G employee, Mason, Ohio, USA.
  • Brian M D'Alessandro
    Canfield Scientific, Parsippany, New Jersey, USA.
  • Anthony Melione
    Canfield Scientific, Parsippany, New Jersey, USA.
  • Douglas Canfield
    Canfield Scientific, Parsippany, New Jersey, USA.
  • Wilma F Bergfeld
    Department of Dermatology, Cleveland Clinic, Cleveland, Ohio, USA.
  • Melissa P Piliang
    Department of Dermatology, Cleveland Clinic, Cleveland, Ohio, USA.
  • Raghu Kainkaryam
    Former P&G employee, Mason, Ohio, USA.
  • Michael G Davis
    The Procter and Gamble Company, Mason, Ohio, USA.

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