@article {Sosalee000892, author = {Bhavana Sosale and Sosale Ramachandra Aravind and Hemanth Murthy and Srikanth Narayana and Usha Sharma and Sahana G V Gowda and Muralidhar Naveenam}, title = {Simple, Mobile-based Artificial Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study}, volume = {8}, number = {1}, elocation-id = {e000892}, year = {2020}, doi = {10.1136/bmjdrc-2019-000892}, publisher = {BMJ Specialist Journals}, abstract = {Introduction The aim of this study is to evaluate the performance of the offline smart phone-based Medios artificial intelligence (AI) algorithm in the diagnosis of diabetic retinopathy (DR) using non-mydriatic (NM) retinal images.Methods This cross-sectional study prospectively enrolled 922 individuals with diabetes mellitus. NM retinal images (disc and macula centered) from each eye were captured using the Remidio NM fundus-on-phone (FOP) camera. The images were run offline and the diagnosis of the AI was recorded (DR present or absent). The diagnosis of the AI was compared with the image diagnosis of five retina specialists (majority diagnosis considered as ground truth).Results Analysis included images from 900 individuals (252 had DR). For any DR, the sensitivity and specificity of the AI algorithm was found to be 83.3\% (95\% CI 80.9\% to 85.7\%) and 95.5\% (95\% CI 94.1\% to 96.8\%). The sensitivity and specificity of the AI algorithm in detecting referable DR (RDR) was 93\% (95\% CI 91.3\% to 94.7\%) and 92.5\% (95\% CI 90.8\% to 94.2\%).Conclusion The Medios AI has a high sensitivity and specificity in the detection of RDR using NM retinal images.}, URL = {https://drc.bmj.com/content/8/1/e000892}, eprint = {https://drc.bmj.com/content/8/1/e000892.full.pdf}, journal = {BMJ Open Diabetes Research and Care} }