Computer Vision Identification of Trachomatous Inflammation-Follicular Using Deep Learning
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.
Authors
- Tibebe Birhanu (ORCID: https://orcid.org/0000-0002-7065-1381)
- Scott D. Nash (ORCID: https://orcid.org/0000-0001-5741-8537)
- Fisseha Admassu
- Zerihun Tadesse (ORCID: https://orcid.org/0000-0002-1403-7651)
- Dionna M. Wittberg (ORCID: https://orcid.org/0000-0002-9887-5993)
- Meskerem Shibiru
- Habib Getachew
- Travis K. Redd (ORCID: https://orcid.org/0000-0001-9859-0317)
- Endale Kabtu
- Solomon Aragie (ORCID: https://orcid.org/0000-0003-4967-9627)
- Thomas M. Lietman (ORCID: https://orcid.org/0000-0001-8216-0240)
- Bilen Wondimteka
- Banchalem Getnet
- Ashlin Joye
- Dagnachew Hailu (ORCID: https://orcid.org/0000-0002-1066-7197)
- Marissa Firlie (ORCID: https://orcid.org/0000-0001-9501-5069)
- Social Beyecha
- Seid Abdu (ORCID: https://orcid.org/0000-0002-5850-5947)
- Solomon Tekew
- Adane Dagnew (ORCID: https://orcid.org/0000-0002-4105-6129)
- Jeremy D. Keenan
Institutions
- Carter Center (US)
- Oregon Health & Science University (US)
- University of California, San Francisco (US)
- George Washington University (US)
- San Francisco Foundation (US)
- The Carter Center (ET)
- University of Gondar (ET)
Publication Details
- Journal
- Cornea
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1097/ico.0000000000003701
- Citations
- 1
- Primary Topic
- Cervical Cancer and HPV Research
- Type
- article
- Field-Weighted Citation Impact
- 0.39
Funders
- Research to Prevent Blindness
- National Eye Institute