Classification Criteria for Cytomegalovirus Retinitis

for the Standardization of Uveitis Nomenclature (SUN) Working Group, Douglas A. Jabs, Rubens Belfort Jr, Bahram Bodaghi, Elizabeth Graham, Gary N. Holland, Susan L. Lightman, Neal Oden, Alan G. Palestine, Justine R. Smith, Jennifer E. Thorne, Brett E. Trusko, Russell N. Van Gelder

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The purpose of this study was to determine classification criteria for cytomegalovirus (CMV) retinitis.

Machine learning of cases with CMV retinitis and 4 other infectious posterior/ panuveitides.

Cases of infectious posterior/panuveitides 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/panuveitides. The resulting criteria were evaluated in the validation set.

A total of 803 cases of infectious posterior/panuveitides, including 211 cases of CMV retinitis, were evaluated by machine learning. Key criteria for CMV retinitis included: 1) necrotizing retinitis with indistinct borders due to numerous small satellites; 2) evidence of immune compromise; and either 3) a characteristic clinical appearance, or 4) positive polymerase chain reaction assay results for CMV from an intraocular specimen. Characteristic appearances for CMV retinitis included: 1) wedge-shaped area of retinitis; 2) hemorrhagic retinitis; or 3) granular retinitis. Overall accuracy for infectious posterior/panuveitides was 92.1% in the training set and 93.3% (95% confidence interval: 88.2-96.3) in the validation set. The misclassification rates for CMV retinitis were 6.9% in the training set and 6.3% in the validation set.

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

Original languageEnglish
Pages (from-to)245-254
Number of pages10
JournalAmerican Journal of Ophthalmology
Early online date11 May 2021
Publication statusPublished - Aug 2021


  • Standardization of Uveitis Nomenclature (SUN) Working Group
  • cytomegalovirus (CMV) retinitis
  • panuveitides
  • infectious posterior/ panuveitides
  • multinomial logistic regression
  • misclassification rate


Dive into the research topics of 'Classification Criteria for Cytomegalovirus Retinitis'. Together they form a unique fingerprint.

Cite this