biostudies-literatureKim MCNational Eye InstituteNEI NIH HHSBill and Melinda Gates FoundationSchool of Medicine, University of California, San Franciscoe0210463https://www.ebi.ac.uk/biostudies/studies/S-EPMC6370195biostudies-literatureUnknown14(2)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 classifiePloS oneSensitivity and specificity of computer vision classification of eyelid photographs for programmatic trachoma assessment.PMC6370195RAPEY016214P30 EY002162U10 EY01621448027Porco TCKim MCRyner AMGaynor BDOkada KAmza ATadesse ZCotter SYLietman TMKeenan JDfalseSensitivity and specificity of computer vision classification of eyelid photographs for programmatic trachoma assessment.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 classifie2019-01-01T00:00:00Z20192025-05-29T20:03:36.547Z2019-06-06T20:51:06ZS-EPMC63701953074263910.1371/journal.pone.0210463