Trial reportPloS one2019
Sensitivity and specificity of computer vision classification of eyelid photographs for programmatic trachoma assessment.
Trial report in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Potential of machine learning for prevention and control of neglected tropical diseases: a scoping review.Communications medicine · 2026Article
- Scaling trachoma surveillance in endemic areas using machine learning.BMC infectious diseases · 2026Article
- Identifying Borderline Trachoma Grades Using a Three-Latent Class Model.The American journal of tropical medicine and hygiene · 2025Article
- A Virtual Reading Center Model Using Crowdsourcing to Grade Photographs for Trachoma: Validation Study.Journal of medical Internet research · 2023Article
- Artificial intelligence and machine learning in ophthalmology: A review.Indian journal of ophthalmology · 2023Review
- Detection of trachoma using machine learning approaches.PLoS neglected tropical diseases · 2022Article
- Trachoma.Nature reviews. Disease primers · 2022Review
- Evaluation of photography using head-mounted display technology (ICAPS) for district Trachoma surveys.PLoS neglected tropical diseases · 2021Observational
- Trachoma Prevalence After Discontinuation of Mass Azithromycin Distribution.The Journal of infectious diseases · 2020Article
- Artificial intelligence in cornea and ocular surface diseases.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
- Development and deployment of a smartphone application for diagnosing trachoma: Leveraging code-free deep learning and edge artificial intelligence.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
BACKGROUND/
aimsTrachoma 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.
methodsA 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 classifier for TF and separately for TI. One hundred images were selected for testing TF and TI, and these images were not used for training.
resultsThe agreement score for TF and TI tasks for the automated algorithm relative to expert graders was κ = 0.44 (95% CI: 0.26 to 0.62, P < 0.001) and κ = 0.69 (95% CI: 0.55 to 0.84, P < 0.001), respectively. DISCUSSION: For assessing the clinical signs of trachoma, a convolutional neural net performed well above chance when tested against expert consensus. Further improvements in specificity may render this method suitable for field use.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.