ArticleScientific reports2024
Analysis of the performance of the CorneAI for iOS in the classification of corneal diseases and cataracts based on journal photographs.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed.
- Artificial Intelligence for Diagnostic Guidance in Ocular Surface Disorders.Journal of clinical medicine · 2026Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- MCOA: A Comprehensive Multimodal Dataset for Advancing Deep Learning in Corneal Opacity Assessment.Scientific data · 2025Article
- A Domain-Specific Pretrained Model for Detecting Malignant and Premalignant Ocular Surface Tumors: A Multicenter Model Development and Evaluation Study.Research (Washington, D.C.) · 2025Article
- Emerging diagnostic modalities in microbial keratitis: Beyond culture and smear.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
7 authors.
Funding
Abstract
CorneAI for iOS is an artificial intelligence (AI) application to classify the condition of the cornea and cataract into nine categories: normal, infectious keratitis, non-infection keratitis, scar, tumor, deposit, acute primary angle closure, lens opacity, and bullous keratopathy. We evaluated its performance to classify multiple conditions of the cornea and cataract of various races in images published in the Cornea journal. The positive predictive value (PPV) of the top classification with the highest predictive score was 0.75, and the PPV for the top three classifications exceeded 0.80. For individual diseases, the highest PPVs were 0.91, 0.73, 0.42, 0.72, 0.77, and 0.55 for infectious keratitis, normal, non-infection keratitis, scar, tumor, and deposit, respectively. CorneAI for iOS achieved an area under the receiver operating characteristic curve of 0.78 (95% confidence interval [CI] 0.5-1.0) for normal, 0.76 (95% CI 0.67-0.85) for infectious keratitis, 0.81 (95% CI 0.64-0.97) for non-infection keratitis, 0.55 (95% CI 0.41-0.69) for scar, 0.62 (95% CI 0.27-0.97) for tumor, and 0.71 (95% CI 0.53-0.89) for deposit. CorneAI performed well in classifying various conditions of the cornea and cataract when used to diagnose journal images, including those with variable imaging conditions, ethnicities, and rare cases.
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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.