ArticleCornea2020
Algorithm Variability in Quantification of Epithelial Defect Size in Microbial Keratitis Images.
Article in Cornea, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04420962 (Quantifying Microbial Keratitis to Predict Outcomes), which is not on this map. Cited by 1 paper.
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.
Quantifying Microbial Keratitis to Predict Outcomes
Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
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 at 2 institutions in 3 countries.
Funding
Abstract
purposeTo investigate the sources of measurement variability when quantifying the morphology of microbial keratitis (MK) from slit-lamp photography (SLP) images using a semiautomated, image-analysis algorithm.
methodsProspectively enrolled patients with MK underwent SLP to obtain images of their epithelial defects (ED). Eyes were stained with fluorescein and imaged multiple times under blue light, at low and high magnifications. A masked research assistant chose the 3 best images and annotated each 3 times to provide seed regions corresponding to ED and healthy cornea. The algorithm returned the ED area for each seeded image. Eyes without EDs and algorithm failures were excluded. Variance components were estimated with a random effects model and intraclass correlation coefficients estimated with intragrader reliability.
resultsA total of 42 eyes from 42 MK participants were photographed. After excluding poor quality images, eyes with no EDs, and algorithm failures, 34 patients with 92 images and 274 seeds were analyzed. No significant differences in the average ED area were found between seedings or high- versus low-SLP magnifications (all P > 0.5, paired t tests). Minimal measurement variability was because of image (0.9%), magnification (0.2%), or seed (0.1%). Most variability was attributable to differences in ED sizes between patients (85.2%). 13.7% of variability was unexplained. Multiple iterations of the algorithm on the same image showed good consistency (intraclass correlation coefficient = 0.98, 95% confidence interval, 0.97-0.99).
conclusionsImage-analysis algorithms showed good reliability for measuring the ED area from SLP images. Most measurement variability was because of between-patient differences, not imaging settings or application of the algorithm by the user.
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Registered trials
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