Sensitivity and specificity of computer vision classification of eyelid photographs for programmatic trachoma assessment.

Journal: PloS one
PMID: 30742639

Abstract

BACKGROUND/AIMS: Trachoma programs base treatment decisions on the community prevalence of the clinical signs of trachoma, assessed by direct examination of the conjunctiva. Automated assessment could be more standardized and more cost-effective. We tested the hypothesis that an automated algorithm could classify eyelid photographs better than chance.

Authors

  • Matthew C Kim
    Francis I. Proctor Foundation, University of California San Francisco, San Francisco, CA, United States of America.
  • Kazunori Okada
    Department of Computer Science, San Francisco State University, San Francisco, CA, United States of America.
  • Alexander M Ryner
    Francis I. Proctor Foundation, University of California San Francisco, San Francisco, CA, United States of America.
  • Abdou Amza
    Programme FSS/Université Abdou Moumouni de Niamey, Programme National de Santé Oculaire, Niamey, Niger.
  • Zerihun Tadesse
    Carter Center, Addis Ababa, Ethiopia.
  • Sun Y Cotter
    Francis I. Proctor Foundation, University of California San Francisco, San Francisco, CA, United States of America.
  • Bruce D Gaynor
    Francis I. Proctor Foundation, University of California San Francisco, San Francisco, CA, United States of America.
  • Jeremy D Keenan
    Francis I. Proctor Foundation, University of California San Francisco, San Francisco, CA, United States of America.
  • Thomas M Lietman
    Francis I. Proctor Foundation, University of California San Francisco, San Francisco, CA, United States of America.
  • Travis C Porco
    Francis I. Proctor Foundation, University of California San Francisco, San Francisco, CA, United States of America.

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