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

Jarek P Sacha1, Tamara L Caterino1, Brian K Fisher1

  • 1The Procter and Gamble Company, Mason, Ohio, USA.

International Journal of Cosmetic Science
|August 24, 2021
PubMed
Summary

Automated hair counting and length measurement using deep learning significantly reduces analysis time and cost for phototrichogram images. This AI-driven method offers high accuracy and speed, benefiting hair growth research.

Keywords:
computer modellinghair growthhair treatment

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Area of Science:

  • Dermatology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • Accurate hair quantification is crucial for evaluating hair growth and retention therapies.
  • Manual analysis of phototrichogram images for hair counting and length measurement is time-consuming, labor-intensive, and costly.
  • Existing methods often analyze only a fraction of available images due to resource constraints.

Purpose of the Study:

  • To develop an automated method for rapid and accurate hair counting and length measurement from phototrichogram images.
  • To significantly increase the speed and throughput of hair analysis in clinical studies.
  • To reduce the cost associated with analyzing large volumes of phototrichogram data.

Main Methods:

  • A dataset of 288 manually annotated phototrichogram images was created for training.
  • A custom deep convolutional neural network architecture and image processing algorithms were designed.
  • The algorithm's performance was validated against the semi-manual Canfield's Hair Metrix method.

Main Results:

  • Deep neural networks enabled the development of a machine learning algorithm for rapid hair analysis.
  • The automated algorithm provides fast, fully automated hair counting and length measurement from scalp phototrichograms.
  • High agreement was observed between the algorithm's measurements and human-assisted analysis (ground truth).

Conclusions:

  • A reproducible, accurate, and fast algorithm for phototrichogram analysis has been developed and deployed.
  • The automated method requires minimal manual intervention and recurring costs after deployment.
  • This approach allows for the analysis of numerous images, reducing study costs and significantly shortening analysis timelines.