Classification Criteria For Multiple Evanescent White Dot Syndrome.

Abstract

The purpose of this study was to determine classification criteria for multiple evanescent white dot syndrome (MEWDS). Machine learning of cases with MEWDS and 8 other posterior uveitides. 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 in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior, or panuveitides. The resulting criteria were evaluated in the validation set. A total of 1,068 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. Misclassification rates for MEWDS were 7% in the training set and 0% in the validation set. The criteria for MEWDS had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.