Performance of a Retinal Imaging Camera With On-Device Intelligence for Primary Care: Retrospective Study - Matthew Silvestrini; Clarissa Lui; Anil Patwardhan; Ying Chen; Tayyeba Ali; Elie Glik; Honglei Wu; Brian Levinstein; Adrianna Wenz; Nathan Shemonski; Lin Yang; Ian Atkinson; et al. (JMIR Formative Research)
Abstract BackgroundAccess to screening continues to be a barrier for the early detection of diabetic retinopathy (DR). Primary care–based diabetic retinopathy screening could improve access, but operational challenges, such as cost and workflow management, hamper the widespread adoption of retinal camera systems in primary care clinics in the United States. ObjectiveThis study aimed to develop and evaluate a retinal screening system suitable for integration into a primary care workflow. MethodsWe developed a nonmydriatic, 45° field imaging retinal camera system, the Verily Numetric Retinal Camera (VNRC; Verily Life Sciences LLC), able to generate high-fidelity retinal images enabled by on-device intelligent features. The VNRC output flows into cloud-based software that accepts and routes digitized images for grading. We evaluated the performance and usability of the VNRC in 2 studies. A retrospective performance study compared the performance of VNRC against a reference camera (Crystalvue NFC-700 [Crystalvue Medical]) as well as the correlation between VNRC capture status and gradability (as determined by ophthalmologist graders). The usability study simulated a primary care setting for a combined cohort of trained and untrained users (corresponding to patients in the simulation) and operators (corresponding to health care personnel in the simulation), where respondents completed a questionnaire about their user experience after attempting to capture images with the VNRC. ResultsIn the comparative performance study (N=108, K=206 images), a total of 98.5% (203/206) of images captured by the VNRC were graded as sufficient for clinical interpretation compared to 97.1% (200/206) of Crystalvue NFC-700 images (difference in proportion was 0.015, 95% CI –0.007 to 0.033). In the quality control algorithm evaluation (N=172, K=343 images), we found a positive association (φ ConclusionsOur findings about the performance and usability of this retinal camera system support its deployment as an integrated end-to-end retinal service for primary care. These results warrant additional studies to fully characterize real-world usability across a wider and diverse set of primary care clinics.