ArticleAmerican journal of ophthalmology2021
Development of Classification Criteria for the Uveitides.
Article in American journal of ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of clinical practice guidelines for uveitis.BMJ open ophthalmology · 2023Pooled it
- Early vs Deferred Non-Messenger RNA COVID-19 Vaccination Among Chinese Patients With a History of Inactive Uveitis: A Randomized Clinical Trial.JAMA network open · 2023Trial
- The Role of MicroRNAs and Other Molecular Biomarkers in Uveitis: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- Corneal and tear film immunology in anterior uveitis: protocol for a systematic review and meta-analysis.BMJ open · 2026Article
- Review
- Vaccine-Associated Anterior Uveitis.Vaccines · 2026Review
- Treatment of a Large Cohort of Childhood Chronic Noninfectious Uveitis in a Multicentric Large Study: Adalimumab Versus Methotrexate as First-Line Therapy.Arthritis & rheumatology (Hoboken, N.J.) · 2026Article
- Cognitive bias and clinical acumen in uveitis practice.Indian journal of ophthalmology · 2026Article
- Single-cell analysis highlights the role of CD4Biology direct · 2026Article
- Clinical Assessment of a Highly Multiplexed Panel Assay for the Diagnosis of Infectious Uveitis.American journal of ophthalmology · 2026Article
- Uveitis of undifferentiated etiology: insights into diagnosis and evolution from a cohort of 338 patients.BMC ophthalmology · 2026Article
- Article
- Are the SUN Criteria Sufficient for Tubercular Uveitis? Findings From a Multinational Registry Study.Investigative ophthalmology & visual science · 2026Observational
- Explainable machine learning-based identification of transcriptomic biomarkers in CD1c+ dendritic cells for non-infectious uveitis: an integrative analysis of bulk RNA-seq data.Frontiers in bioinformatics · 2026Article
- When the Eye Reveals Disseminated Tuberculosis: A Pediatric Case of Endophthalmitis With Severe CNS Involvement.Case reports in ophthalmological medicine · 2026Article
- Sarcoid Uveitis with Choroidal Involvement.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- Impact of switching from the originator adalimumab to a biosimilar: a retrospective cohort study.BMC immunology · 2025Article
- Compositional variation in eye-infiltrating immune cells distinguishes human uveitis subtypes.iScience · 2025Article
- Recurrent strokes as the sole manifestation of antineutrophil cytoplasmic antibody associated vasculitis in a patientwith panuveitis: a case report.Journal of ophthalmic inflammation and infection · 2025Article
- Therapeutic drug monitoring and neutralizing anti-drug antibody detection to optimize TNF-alpha inhibitor treatment for uveitis.Frontiers in ophthalmology · 2025Article
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Abstract
purposeTo develop classification criteria for 25 of the most common uveitides.
designMachine learning using 5,766 cases of 25 uveitides.
methodsCases were collected in an informatics-designed preliminary database. Using formal consensus techniques, a final database was constructed of 4,046 cases achieving supermajority agreement on the diagnosis. Cases were analyzed within uveitic class and were split into a training set and a validation set. Machine learning used multinomial logistic regression with lasso regularization on the training set to determine a parsimonious set of criteria for each disease and to minimize misclassification rates. The resulting criteria were evaluated in the validation set. Accuracy of the rules developed to express the machine learning criteria was evaluated by a masked observer in a 10% random sample of cases.
resultsOverall accuracy estimates by uveitic class in the validation set were as follows: anterior uveitides 96.7% (95% confidence interval [CI] 92.4, 98.6); intermediate uveitides 99.3% (95% CI 96.1, 99.9); posterior uveitides 98.0% (95% CI 94.3, 99.3); panuveitides 94.0% (95% CI 89.0, 96.8); and infectious posterior uveitides / panuveitides 93.3% (95% CI 89.1, 96.3). Accuracies of the masked evaluation of the "rules" were anterior uveitides 96.5% (95% CI 91.4, 98.6) intermediate uveitides 98.4% (91.5, 99.7), posterior uveitides 99.2% (95% CI 95.4, 99.9), panuveitides 98.9% (95% CI 94.3, 99.8), and infectious posterior uveitides / panuveitides 98.8% (95% CI 93.4, 99.9).
conclusionsThe classification criteria for these 25 uveitides had high overall accuracy (ie, low misclassification rates) and seemed to perform well enough for use in clinical and translational research.
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