ArticleCornea2018
Novel Image-Based Analysis for Reduction of Clinician-Dependent Variability in Measurement of the Corneal Ulcer Size.
Article in Cornea, 2018. 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 18 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.
Quantifying Microbial Keratitis to Predict Outcomes
Who cites it
18 citing papers in PubMed, 29 citations in OpenAlex.
- Association of Deep Learning Imaging Algorithm Measures of Microbial Keratitis With Vision Outcomes.Cornea · 2026Article
- Long-Term Graft Survival Following Penetrating Keratoplasty in Eyes with Preoperative Corneal Neovascularization.Ophthalmology and therapy · 2026Article
- Deep Learning-Based Classification of Slit-Lamp Photograph Quality in Microbial Keratitis.Ophthalmology science · 2026Article
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Self-knowledge distillation-empowered directional connectivity transformer for microbial keratitis biomarkers segmentation on slit-lamp photography.Medical image analysis · 2025Article
- Artificial intelligence-assisted diagnosis of ocular surface diseases.Frontiers in cell and developmental biology · 2023Review
- Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2022Review
- Assessment of Keratitis Severity Using Quantitative Image Analysis in an In Vivo Murine Model of Staphylococcus aureus Bacterial Keratitis.Translational vision science & technology · 2022Article
- Case report: Utilization of neutral density filters for densitometry analysis of dense corneal opacities.American journal of ophthalmology case reports · 2022Article
- Open-Source Automatic Biomarker Measurement on Slit-Lamp Photography to Estimate Visual Acuity in Microbial Keratitis.Translational vision science & technology · 2021Article
- Artificial Intelligence in Cornea, Refractive Surgery, and Cataract: Basic Principles, Clinical Applications, and Future Directions.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) · 2021Review
- A novel approach to anterior segment imaging with smartphones in the COVID-19 era.Indian journal of ophthalmology · 2021Article
- Open-Source Automatic Segmentation of Ocular Structures and Biomarkers of Microbial Keratitis on Slit-Lamp Photography Images Using Deep Learning.IEEE journal of biomedical and health informatics · 2021Article
- Measurement Reliability for Keratitis Morphology.Cornea · 2020Article
- Use of 'U-shaped tool for follow up of corneal ulcer cases in the COVID-19 pandemic.Indian journal of ophthalmology · 2020Article
- Could telehealth help eye care practitioners adapt contact lens services during the COVID-19 pandemic?Contact lens & anterior eye : the journal of the British Contact Lens Association · 2020Article
- Algorithm Variability in Quantification of Epithelial Defect Size in Microbial Keratitis Images.Cornea · 2020Article
- Artificial intelligence in cornea and ocular surface diseases.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
8 authors at 3 institutions in 3 countries.
Funding
Abstract
purposeTo assess variability in corneal ulcer measurements between ophthalmologists and reduce clinician-dependent variability using semiautomated segmentation of the ulcer from photographs.
methodsThree ophthalmologists measured 50 patients' eyes for epithelial defects (EDs) and the stromal infiltrate (SI) size using slit-lamp (SL) calipers. SL photographs were obtained. An algorithm was developed for semiautomatic segmenting of the ED and SI in the photographs. Semiautomatic segmentation was repeated 3 times by different users (2 ophthalmologists and 1 trainee). Clinically significant variability was assessed with intraclass correlation coefficients (ICCs) and the percentage of pairwise measurements differing by ≥0.5 mm. Semiautomatic segmentation measurements were compared with manual delineation of the image by a corneal specialist (gold standard) using Dice similarity coefficients.
resultsOphthalmologists' reliability in measurements by SL calipers had an ICC from 0.84 to 0.88 between examiners. Measurements by semiautomatic segmentation had an ICC from 0.96 to 0.98. SL measures of ulcers by clinical versus semiautomatic segmentation measures differed by ≥0.5 mm in 24% to 38% versus 8% to 28% (ED height); 30% to 52% versus 12% to 34% (ED width); 26% to 38% versus 10% to 32% (SI height); and 38% to 58% versus 14% to 34% (SI width), respectively. Average Dice similarity coefficients between manual and repeated semiautomatic segmentation ranged from 0.83 to 0.86 for the ED and 0.78 to 0.83 for the SI.
conclusionsVariability exists when measuring corneal ulcers, even among ophthalmologists. Photography and computerized methods for quantifying the ulcer size could reduce variability while remaining accurate and impact quantitative measurement endpoints.
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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.