Overall accuracy for posterior uveitides was 92.7% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for APMPPE were 5% in the training set and 0% in the validation set.

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

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

To determine classification criteria for tubulointerstitial nephritis with uveitis (TINU).

Machine learning of cases with TINU and 8 other anterior uveitides.

Cases of anterior uveitides 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 anterior uveitides. The resulting criteria were evaluated on the validation set.

One thousand eighty-three cases of anterior uveitides, including 94 cases of TINU, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for TINU included anterior chamber inflammation and evidence of tubulointerstitial nephritis with either (1) a positive renal biopsy or (2) evidence of nephritis (elevated serum creatinine and/or abnormal urine analysis) and an elevated urine β-2 microglobulin. The misclassification rates for TINU were 1.2% in the training set and 0% in the validation set.

The criteria for TINU had a low misclassification rate and seemed to perform well enough for use in clinical and translational research.

The criteria for TINU had a low misclassification rate and seemed to perform well enough for use in clinical and translational research.

The purpose of this study was to determine classification criteria for multiple sclerosis-associated intermediate uveitis.

Machine learning of cases with multiple sclerosis-associated intermediate uveitis and 4 other intermediate uveitides.

Cases of intermediate uveitides 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 in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated in the validation set.

A total of 589 cases of intermediate uveitides, including 112 cases of multiple sclerosis-associated intermediate uveitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval 96.1-99.9). Key criteria for multiple sclerosis-associated intermediate uveitis included unilateral or bilateral intermediate uveitis and multiple sclerosis diagnosed by the McDonald criteria. Key exclusions included syphilis and sarcoidosis. The misclassification rates for multiple sclerosis-associated intermediate uveitis were 0 % in the training set and 0% in the validation set.

The criteria for multiple sclerosis-associated intermediate uveitis had a low misclassification rate and appeared to perform sufficiently well enough for use in clinical and translational research.

The criteria for multiple sclerosis-associated intermediate uveitis had a low misclassification rate and appeared to perform sufficiently well enough for use in clinical and translational research.

To determine classification criteria for juvenile idiopathic arthritis (JIA)-associated chronic anterior uveitis (CAU).

Machine learning of cases with JIA CAU and 8 other anterior uveitides.

Cases of anterior uveitides 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 anterior uveitides. Ethyl 3-Aminobenzoate molecular weight The resulting criteria were evaluated on the validation set.

One thousand eighty-three cases of anterior uveitides, including 202 cases of JIA CAU, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for JIA CAU included (1) chronic anterior uveitis (or, if newly diagnosed, insidious onset) and (2) JIA, except for the systemic, rheumatoid factor-positive polyarthritis, and enthesitis-related arthritis variants. The misclassification rates for JIA CAU were 2.4% in the training set and 0% in the validation set.

The criteria for JIA CAU had a low misclassification rate and seemed to perform well enough for use in clinical and translational research.

The criteria for JIA CAU had a low misclassification rate and seemed to perform well enough for use in clinical and translational research.

To determine classification criteria for syphilitic uveitis.

Machine learning of cases with syphilitic uveitis and 24 other uveitides.

Cases of anterior, intermediate, posterior, and 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 analyzed by anatomic class, and each class was 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 different uveitic classes. The resulting criteria were evaluated on the validation set.

Two hundred twenty-two cases of syphilitic uveitis were evaluated by machine learning, with cases evaluated against other uveitides in the relevant uveitic class. Key criteria for syphilitic uveitis included a compatible uveitic presentation (anterior uveitis; intermediate uveitis; or posterior or panuveitis with retinal, retinal pigment epithelial, or retinal vascular inflammation) and evidence of syphilis infection with a positive treponemal test.