To determine classification criteria for toxoplasmic retinitis.

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

Cases of infectious posterior uveitides / 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 on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior uveitides / panuveitides. The resulting criteria were evaluated on the validation set.

Eight hundred three cases of infectious posterior uveitides / panuveitides, including 174 cases of toxoplasmic retinitis, were evaluated by machine learning. Key criteria for toxoplasmic retinitis included focal or paucifocal necrotizing retinitis and either positive polymerase chain reaction assay for Toxoplasma gondii from an intraocular specimen or the characteristic clinical picture of a round or oval retinitis lesion proximal to a hyperpigmented and/or atrophic chorioretinal scar. Overall accuracy for infectious posterior uveitides / 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 toxoplasmic retinitis were 8.2% in the training set and 10% in the validation set.

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