Classification Criteria for Multifocal Choroiditis With Panuveitis.

Academic Article


  • PURPOSE: To determine classification criteria for multifocal choroiditis with panuveitis (MFCPU). DESIGN: Machine learning of cases with MFCPU 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 posterior uveitides. The resulting criteria were evaluated on the validation set. RESULTS: One thousand sixty-eight cases of posterior uveitides, including 138 cases of MFCPU, were evaluated by machine learning. Key criteria for MFCPU included (1) multifocal choroiditis with the predominant lesions size >125 ┬Ám in diameter; (2) lesions outside the posterior pole (with or without posterior involvement); and either (3) punched-out atrophic chorioretinal scars or (4) more than minimal mild anterior chamber and/or 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 MFCPU were 15% in the training set and 0% in the validation set. CONCLUSIONS: The criteria for MFCPU had a reasonably low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.
  • Authors

    Published In


  • Adult, Anterior Chamber, Female, Humans, Machine Learning, Male, Middle Aged, Multifocal Choroiditis, Visual Acuity
  • Digital Object Identifier (doi)

    Pubmed Id

  • 8367074
  • Author List

  • Standardization of Uveitis Nomenclature (SUN) Working Group
  • Start Page

  • 152
  • End Page

  • 158
  • Volume

  • 228