A smartphone application (app) shows promise as a noninvasive and novel method for assisting in the diagnosis of dry eye disease (DED), according to a study that found that the app-based measurements of the Japanese version of the Ocular Surface Disease Index (J-OSDI) were equivalent to traditional paper-based measurements.
Participants then utilized the app to provide data on app-based J-OSDI and maximum blink interval (MBI). The study assessed the equivalence between app-based and traditional paper-based J-OSDI through an equivalence test. Additionally, Bland-Altman analysis was employed to evaluate the agreement between app-based and manually measured MBIs. The screening performance of the app was further analyzed using Receiver Operating Characteristic (ROC) analysis.
Results from the study demonstrated that the app-based and paper-based J-OSDIs were found to be equivalent among the 34 participants, with a mean difference of 1.8 (95% CI, −1.4 to 5.0). Bland-Altman analysis for agreement on MBI between the app and manual measurements revealed biases of −0.08 (n = 83, limits of agreement: −0.76 to 0.60). The app exhibited a positive predictive value of 91.3%, and the area under the ROC curve was 0.910 among the 84 participants.
These findings suggest that the smartphone app presents a novel and noninvasive method for aiding in the diagnosis of dry eye disease. The positive results from the equivalence and agreement tests, along with the high predictive value and ROC curve performance, indicate the app’s potential as an effective tool in the early detection and monitoring of DED
Reference
Okumura Y, et al. A Feasibility of a Smartphone Application to Assist Diagnosis of Dry Eye. Poster Presented at: AAO 2023.
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