PURPOSE: The purpose of this study was to determine classification criteria for sympathetic ophthalmia. DESIGN: Machine learning of cases with sympathetic ophthalmia and 5 other panuveitides. METHODS: Cases of panuveitides 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 in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the panuveitides. The resulting criteria were evaluated in the validation set. RESULTS: A total of 1,012 cases of panuveitides, including 110 cases of sympathetic ophthalmia, were evaluated by machine learning. The overall accuracy for panuveitides was 96.3% in the training set and 94.0% in the validation set (95% confidence interval: 89.0-96.8). Key criteria for sympathetic ophthalmia included bilateral uveitis with 1) a history of unilateral ocular trauma or surgery and 2) an anterior chamber and vitreous inflammation or a panuveitis with choroidal involvement. The misclassification rates for sympathetic ophthalmia were 4.2% in the training set and 6.7% in the validation set. CONCLUSIONS: The criteria for sympathetic ophthalmia had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.
PURPOSE: The purpose of this study was to determine classification criteria for sympathetic ophthalmia. DESIGN: Machine learning of cases with sympathetic ophthalmia and 5 other panuveitides. METHODS: Cases of panuveitides 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 in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the panuveitides. The resulting criteria were evaluated in the validation set. RESULTS: A total of 1,012 cases of panuveitides, including 110 cases of sympathetic ophthalmia, were evaluated by machine learning. The overall accuracy for panuveitides was 96.3% in the training set and 94.0% in the validation set (95% confidence interval: 89.0-96.8). Key criteria for sympathetic ophthalmia included bilateral uveitis with 1) a history of unilateral ocular trauma or surgery and 2) an anterior chamber and vitreous inflammation or a panuveitis with choroidal involvement. The misclassification rates for sympathetic ophthalmia were 4.2% in the training set and 6.7% in the validation set. CONCLUSIONS: The criteria for sympathetic ophthalmia had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.
Authors: C C Chan; R B Nussenblatt; L S Fujikawa; A G Palestine; G Stevens; L M Parver; M W Luckenbach; T Kuwabara Journal: Ophthalmology Date: 1986-05 Impact factor: 12.079
Authors: Anat Galor; Janet L Davis; Harry W Flynn; William J Feuer; Sander R Dubovy; Vikram Setlur; Muge R Kesen; Debra A Goldstein; Howard H Tessler; Irina Bykhovskaya Ganelis; Douglas A Jabs; Jennifer E Thorne Journal: Am J Ophthalmol Date: 2009-08-07 Impact factor: 5.258