LAUSR

PURPOSE To determine classification criteria for Fuchs uveitis syndrome. DESIGN Machine learning of cases with Fuchs uveitis syndrome and 8 other anterior uveitides. METHODS Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set. RESULTS One thousand eighty-three cases of anterior uveitides, including 146 cases of Fuchs uveitis syndrome, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for Fuchs uveitis syndrome included unilateral anterior uveitis with or without vitritis and either: 1) heterochromia or 2) unilateral diffuse iris atrophy and stellate keratic precipitates. The overall accuracy for anterior uveitides was 97.5% in the training set (95% confidence interval [CI] 96.3, 98.4) and 96.7% in the validation set (95% CI 92.4, 98.6). The misclassification rates for FUS were 4.7% in the training set and 5.5% in the validation set, respectively. CONCLUSIONS The criteria for Fuchs uveitis syndrome had a low misclassification rate and appeared to perform well enough for use in clinical and translational research.