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A Virtual Reading Center Model Using Crowdsourcing to Grade Photographs for Trachoma: Validation Study.

Christopher J Brady1, R Chase Cockrell2, Lindsay R Aldrich3

  • 1Division of Ophthalmology, Department of Surgery, Larner College of Medicine at The University of Vermont, Burlington, VT, United States.

Journal of Medical Internet Research
|April 6, 2023
PubMed
Summary
This summary is machine-generated.

A cloud-based virtual reading center (VRC) using crowdsourcing accurately identified trachomatous inflammation-follicular (TF) in low-prevalence settings. This approach offers a rapid and cost-effective method for trachoma surveillance and public health decision-making.

Keywords:
Amazon Mechanical Turkcloud-basedcrowdsourcingdetectiondiagnosisdiagnosticsdisease gradingdisease identificationimage analysisimage gradingimage interpretationophthalmic photographyophthalmologytelemedicinetrachomatrachomatous inflammation—follicular

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Area of Science:

  • Ophthalmology
  • Public Health
  • Digital Health

Background:

  • Trachoma elimination efforts are challenged by declining grader expertise in identifying active disease (trachomatous inflammation-follicular [TF]).
  • Accurate TF grading is crucial for public health decisions regarding continued or reinstated treatment strategies.
  • Telemedicine for trachoma surveillance requires reliable image interpretation, which is hindered by poor connectivity in endemic regions.

Purpose of the Study:

  • To develop and validate a cloud-based virtual reading center (VRC) model utilizing crowdsourcing for interpreting trachoma images.
  • To assess the accuracy and efficiency of this VRC model in identifying TF.

Main Methods:

  • The Amazon Mechanical Turk (AMT) platform recruited lay graders to interpret 2299 images from a smartphone-based camera system.
  • Crowdsourcing scores were aggregated, and an optimal cutoff was determined to maximize kappa agreement and TF prevalence estimation.
  • A tiered approach with skilled overread of positive cases was implemented to enhance accuracy.

Main Results:

  • Over 16,000 grades were obtained in about an hour for $1098.
  • The VRC achieved 95% sensitivity and 87% specificity for TF in the training set (kappa=0.797).
  • With skilled overreads, specificity improved to 99%, reducing the grader burden by over 80% and achieving a kappa of 0.685.

Conclusions:

  • A VRC model employing crowdsourcing followed by skilled grading of positive images can rapidly and accurately detect TF in low-prevalence settings.
  • This model supports further validation for trachoma surveillance and prevalence estimation using field-acquired images.
  • Prospective field testing is necessary to confirm the diagnostic acceptability of this VRC model in real-world, low-prevalence surveys.