Classification Criteria for Behçet Disease Uveitis

Am J Ophthalmol. 2021 Aug:228:80-88. doi: 10.1016/j.ajo.2021.03.058. Epub 2021 May 11.


Purpose: To determine classification criteria for Behçet disease uveitis.

Design: Machine learning of cases with Behçet disease 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 on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation set.

Results: One thousand twelve cases of panuveitides, including 194 cases of Behçet disease with uveitis, 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 Behçet disease uveitis were a diagnosis of Behçet disease using the International Study Group for Behçet Disease criteria and a compatible uveitis, including (1) anterior uveitis; (2) anterior chamber and vitreous inflammation; (3) posterior uveitis with retinal vasculitis and/or focal infiltrates; or (4) panuveitis with retinal vasculitis and/or focal infiltrates. The misclassification rates for Behçet disease uveitis were 0.6% in the training set and 0% in the validation set, respectively.

Conclusions: The criteria for Behçet disease uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.

Publication types

  • Comparative Study
  • Multicenter Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Behcet Syndrome / classification
  • Behcet Syndrome / complications*
  • Behcet Syndrome / diagnosis
  • Consensus*
  • Female
  • Humans
  • Machine Learning*
  • Male
  • Retrospective Studies
  • Uveitis / classification*
  • Uveitis / diagnosis
  • Uveitis / etiology
  • Young Adult