Classification criteria for multiple evanescent white dot syndrome

PURPOSE: To determine classification criteria for multiple evanescent white dot syndrome (MEWDS).

DESIGN: Machine learning of cases with MEWDS and 8 other posterior uveitides.

METHODS: Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on 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 infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set.

RESULTS: One thousand sixty-eight cases of posterior uveitides, including 51 cases of MEWDS, were evaluated by machine learning. Key criteria for MEWDS included: 1) multifocal gray white chorioretinal spots with foveal granularity; 2) characteristic imaging on fluorescein angiography ("wreath-like" hyperfluorescent lesions) and/or optical coherence tomography (hyper-reflective lesions extending from retinal pigment epithelium through ellipsoid zone into the retinal outer nuclear layer); and 3) absent to mild anterior chamber and vitreous inflammation. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for MEWDS were 7% in the training set and 0% in the validation set.

CONCLUSIONS: The criteria for MEWDS had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.