Multicenter, Head-to-Head, Real-World Validation Study of Seven Automated Artificial Intelligence Diabetic Retinopathy Screening Systems.

OBJECTIVE: With rising global prevalence of diabetic retinopathy (DR), automated DR screening is needed for primary care settings. Two automated artificial intelligence (AI)-based DR screening algorithms have U.S. Food and Drug Administration (FDA) approval. Several others are under consideration while in clinical use in other countries, but their real-world performance has not been evaluated systematically. We compared the performance of seven automated AI-based DR screening algorithms (including one FDA-approved algorithm) against human graders when analyzing real-world retinal imaging data. RESEARCH DESIGN AND METHODS: This was a multicenter, noninterventional device validation study evaluating a total of 311,604 retinal images from 23,724 veterans who presented for teleretinal DR screening at the Veterans Affairs (VA) Puget Sound Health Care System (HCS) or Atlanta VA HCS from 2006 to 2018. Five companies provided seven algorithms, including one with FDA approval, that independently analyzed all scans, regardless of image quality. The sensitivity/specificity of each algorithm when classifying images as referable DR or not were compared with original VA teleretinal grades and a regraded arbitrated data set. Value per encounter was estimated. RESULTS: Although high negative predictive values (82.72-93.69%) were observed, sensitivities varied widely (50.98-85.90%). Most algorithms performed no better than humans against the arbitrated data set, but two achieved higher sensitivities, and one yielded comparable sensitivity (80.47%, P = 0.441) and specificity (81.28%, P = 0.195). Notably, one had lower sensitivity (74.42%) for proliferative DR (P = 9.77 × 10-4) than the VA teleretinal graders. Value per encounter varied at $15.14-$18.06 for ophthalmologists and $7.74-$9.24 for optometrists. CONCLUSIONS: The DR screening algorithms showed significant performance differences. These results argue for rigorous testing of all such algorithms on real-world data before clinical implementation.

OFF bipolar cell density varies by subtype, eccentricity, and along the dorsal ventral axis in the mouse retina.

The neural retina is organized along central-peripheral, dorsal-ventral, and laminar planes. Cellular density and distributions vary along the central-peripheral and dorsal-ventral axis in species including primates, mice, fish, and birds. Differential distribution of cell types within the retina is associated with sensitivity to different types of damage that underpin major retinal diseases, including macular degeneration and glaucoma. Normal variation in retinal distribution remains unreported for multiple cell types in widely used research models, including mouse. Here we map the distribution of all known OFF bipolar cell (BC) populations and horizontal cells. We report significant variation in the distribution of OFF BC populations and horizontal cells along the dorsal-ventral and central-peripheral axes of the retina. Distribution patterns are much more pronounced for some populations of OFF BC cells than others and may correspond to the cell type's specialized functions.