Comparison of automated and expert human grading of diabetic retinopathy using smartphone-based retinal photography.

Tyson N KimMichael T AabergPatrick LiJose R DavilaMalavika BhaskaranandSandeep BhatChaithanya RamachandraKaushal SolankiFrankie MyersClay ReberRohan JalalizadehTodd P MargolisDaniel A FletcherYannis M Paulus

Published in: Eye (London, England) (2020)

Retinal photography with RetinaScope combined with automated interpretation by EyeArt achieved a lower sensitivity but higher specificity than trained expert graders. Feasibility testing was performed using non-ophthalmic personnel in a retina clinic with high disease burden. Additional studies are needed to assess efficacy of screening diabetic patients from general population.

Keyphrases

diabetic retinopathy
optical coherence tomography
deep learning
machine learning
high throughput
endothelial cells
clinical practice
primary care
induced pluripotent stem cells
resistance training
pluripotent stem cells
optic nerve
body composition
clinical evaluation