Uvemaster: A mobile app-based decision support system for the differential diagnosis of uveitis

Abstract

PURPOSE. To examine the diagnostic accuracy and performance of Uvemaster, a mobile application (app) or diagnostic decision support system (DDSS) for uveitis. The app contains a large database of knowledge including 88 uveitis syndromes each with 76 clinical items, both ocular and systemic (total 6688) and their respective prevalences, and displays a differential diagnoses list (DDL) ordered by sensitivity, specificity, or positive predictive value (PPV).

METHODS. In this retrospective case-series study, diagnostic accuracy (percentage of cases for which a correct diagnosis was obtained) and performance (percentage of cases for which a specific diagnosis was obtained) were determined in reported series of patients originally diagnosed by a uveitis specialist with specific uveitis (N = 88) and idiopathic uveitis (N = 71), respectively.

RESULTS. Diagnostic accuracy was 96.6% (95% confidence interval [CI], 93.2–100). By sensitivity, the original diagnosis appeared among the top three in the DDL in 90.9% (95% CI, 84.1–96.6) and was the first in 73.9% (95% CI, 63.6–83.0). By PPV, the original diagnosis was among the top DDL three in 62.5% (95% CI, 51.1–71.6) and the first in 29.5% (95% CI, 20.5– 38.6; P < 0.001). In 71 (31.1%) patients originally diagnosed with idiopathic uveitis, 19 new diagnoses were made reducing this series to 52 (22.8%) and improving by 8.3% the new rate of diagnosed specific uveitis cases (performance = 77.2%; 95% CI, 71.1–82.9).

CONCLUSIONS. Uvemaster proved accurate and based on the same clinical data was able to detect more cases of specific uveitis than the original clinician only–based method.