Classification criteria for punctate inner choroiditis

ABSTRACT Purpose To determine classification criteria for punctate inner choroiditis (PIC). Design Machine learning of cases with PIC 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 144 cases of PIC, were evaluated by machine learning. Key criteria for PIC included: 1) “punctate” appearing choroidal spots Conclusions The criteria for PIC had a reasonably low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.