Computer Vision Identification of Trachomatous Inflammation-Follicular Using Deep Learning

Authors

Ashlin S. Joye, Casey Eye Institute, Oregon Health and Science University, Portland, OR.
Marissa G. Firlie, George Washington University, School of Medicine and Health Sciences, Washington, DC.
Dionna M. Wittberg, Francis I Proctor Foundation, University of California San Francisco, San Francisco, CA.
Solomon Aragie, The Carter Center Ethiopia, Addis Ababa, Ethiopia.
Scott D. Nash, The Carter Center, Atlanta, GA; and.
Zerihun Tadesse, The Carter Center Ethiopia, Addis Ababa, Ethiopia.
Adane Dagnew, The Carter Center Ethiopia, Addis Ababa, Ethiopia.
Dagnachew Hailu, The Carter Center Ethiopia, Addis Ababa, Ethiopia.
Fisseha Admassu, Department of Ophthalmology, University of Gondar, Gondar, Ethiopia.
Bilen Wondimteka, Department of Ophthalmology, University of Gondar, Gondar, Ethiopia.
Habib Getachew, Department of Ophthalmology, University of Gondar, Gondar, Ethiopia.
Endale Kabtu, Department of Ophthalmology, University of Gondar, Gondar, Ethiopia.
Social Beyecha, Department of Ophthalmology, University of Gondar, Gondar, Ethiopia.
Meskerem Shibiru, Department of Ophthalmology, University of Gondar, Gondar, Ethiopia.
Banchalem Getnet, Department of Ophthalmology, University of Gondar, Gondar, Ethiopia.
Tibebe Birhanu, Department of Ophthalmology, University of Gondar, Gondar, Ethiopia.
Seid Abdu, Department of Ophthalmology, University of Gondar, Gondar, Ethiopia.
Solomon Tekew, Department of Ophthalmology, University of Gondar, Gondar, Ethiopia.
Thomas M. Lietman, Francis I Proctor Foundation, University of California San Francisco, San Francisco, CA.
Jeremy D. Keenan, Francis I Proctor Foundation, University of California San Francisco, San Francisco, CA.
Travis K. Redd, Casey Eye Institute, Oregon Health and Science University, Portland, OR.

Document Type

Journal Article

Publication Date

9-20-2024

Journal

Cornea

DOI

10.1097/ICO.0000000000003701

Abstract

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 for human error. This study describes the development and evaluation of machine learning models intended to reduce cost and improve reliability of these surveys. METHODS: Fifty-six thousand seven hundred twenty-five everted eyelid photographs were obtained from 11,358 children of age 0 to 9 years in a single trachoma-endemic region of Ethiopia over a 3-year period. Expert graders reviewed all images from each examination to determine the estimated number of tarsal conjunctival follicles and the degree of trachomatous inflammation-intense. The median estimate of the 3 grader groups was used as the ground truth to train a MobileNetV3 large deep convolutional neural network to detect cases with TF. RESULTS: The classification model predicted a TF prevalence of 32%, which was not significantly different from the human consensus estimate (30%; 95% confidence interval of difference, -2 to +4%). The model had an area under the receiver operating characteristic curve of 0.943, F1 score of 0.923, 88% accuracy, 83% sensitivity, and 91% specificity. The area under the receiver operating characteristic curve increased to 0.995 when interpreting nonborderline cases of TF. CONCLUSIONS: Deep convolutional neural network models performed well at classifying TF and detecting the number of follicles evident in conjunctival photographs. Implementation of similar models may enable accurate, efficient, large-scale trachoma screening. Further validation in diverse populations with varying TF prevalence is needed before implementation at scale.

APA Citation

Joye, Ashlin S.; Firlie, Marissa G.; Wittberg, Dionna M.; Aragie, Solomon; Nash, Scott D.; Tadesse, Zerihun; Dagnew, Adane; Hailu, Dagnachew; Admassu, Fisseha; Wondimteka, Bilen; Getachew, Habib; Kabtu, Endale; Beyecha, Social; Shibiru, Meskerem; Getnet, Banchalem; Birhanu, Tibebe; Abdu, Seid; Tekew, Solomon; Lietman, Thomas M.; Keenan, Jeremy D.; and Redd, Travis K., "Computer Vision Identification of Trachomatous Inflammation-Follicular Using Deep Learning" (2024). GW Authored Works. Paper 5612.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/5612

Department

School of Medicine and Health Sciences Student Works