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  • Title: Classification Criteria For Multiple Evanescent White Dot Syndrome.
    Author: Standardization of Uveitis Nomenclature (SUN) Working Group.
    Journal: Am J Ophthalmol; 2021 Aug; 228():198-204. PubMed ID: 33845025.
    Abstract:
    PURPOSE: The purpose of this study was 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 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. RESULTS: 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. CONCLUSIONS: The criteria for MEWDS had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.
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