ArticleCornea2026
Association of Deep Learning Imaging Algorithm Measures of Microbial Keratitis With Vision Outcomes.
Article in Cornea, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
- Artificial intelligence in microbial keratitis.Indian journal of ophthalmology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
Abstract
purposeTo compare associations between 90-day vision and microbial keratitis-related corneal changes when predicted by an artificial intelligence image-based algorithm or when measured by a clinician.
methodsThis prospective cohort study followed patients with microbial keratitis in the United States and India for up to 90 days. Participants' clinical history, best-corrected visual acuity (BCVA), slitlamp imaging, and anterior exam findings were collected. A novel, artificial intelligence imaging segmentation algorithm was developed to quantify relevant corneal morphologic features, including presenting stromal infiltrate (SI) area and hypopyon presence using corneal photography. Multivariable linear regression models assessed associations of presenting features, including algorithm-predicted measurements, with 90-day logMAR BCVA. Results are reported with model estimates (β) and 95% confidence intervals.
resultsThe study included 479 participants (N = 138 United States, N = 341 India). In the United States, larger algorithm-predicted SI area [β = 0.02 per 1 mm 2 area increase for bacterial ulcers ( P = 0.002); β = 0.09 per 1 mm 2 increase for fungal ulcers ( P < 0.05); and β = 0.25 per 1 mm 2 increase for viral ulcers ( P = 0.0095)] was associated with worse 90-day vision. In India, larger algorithm-predicted SI area (β = 0.05 per 1 mm 2 area increase for fungal ulcers, P < 0.001) and algorithm-predicted hypopyon (β = 0.22, 95%, confidence interval 0.02-0.41; P = 0.0290) were associated with worse 90-day vision. Algorithm-based measurements of SI area and hypopyon presence showed a similar association with 90-day logMAR BCVA, in magnitude and significance, to those using clinicians' measurements.
conclusionsWorse vision outcomes were associated with larger SI area and hypopyon presence when predicted by the imaging segmentation algorithm or measured by clinicians in both cohorts.
Indexed as
Identifiers
What OpenQuestion holds
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.