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

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. METHODS:A total of 1,656 field-collected conjunctival images were obtained from clinical trial participants in Niger and Ethiopia. Images were scored for trachomatous inflammation-follicular (TF) and trachomatous inflammation-intense (TI) according to the simplified World Health Organization grading system by expert raters. We developed an automated procedure for image enhancement followed by application of a convolutional neural net classifie

SUBMITTER: Kim MC 

PROVIDER: S-EPMC6370195 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature