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Trachoma

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

PloS one 30742639
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 ...
Algorithms Artificial Intelligence Conjunctiva Ethiopia Eyelids Humans Image Processing, Computer-Assisted Neural Networks, Computer Niger Photography Prevalence Trachoma
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Computer Vision Identification of Trachomatous Inflammation-Follicular Using Deep Learning.

Cornea 39312712
PURPOSE: Trachoma surveys are used to estimate the prevalence of trachomatous inflammation-follicular (TF) to guide mass antibiotic distribution. These surveys currently rely on human graders, introducing a significant resource burden and potential f...
Child Child, Preschool Conjunctiva Deep Learning Diagnosis, Computer-Assisted Ethiopia Eyelids Female Humans Infant Infant, Newborn Male Neural Networks, Computer Photography Prevalence Reproducibility of Results ROC Curve Sensitivity and Specificity Trachoma
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