A preliminary study of a deep learning-assisted diagnostic system with an artificial intelligence for detection of diabetic retinopathy

AIM: To evaluate a deep learning-assisted diagnostic system with an artificial intelligence for the detection of diabetic retinopathy(DR). METHODS:A total of 186 patients(372 eyes)with diabetes were recruited from January to July 2017. Discrepancies between manual grades and artificial intelligence results were sent to a reading center for arbitration. The sensitivity and specificity in the detection of DR were determined by comparison with artificial intelligence diagnostic system and experts human grading. RESULTS:Based on manual grades, the results as follows: non DR(NDR)in 42 eyes(11.3%), 330 eyes(88.7%)in different stages of DR. Among 330 DR eyes, there were mild non proliferative DR(NPDR)in 62 eyes(16.7%), moderate NPDR in 55 eyes(14.8%), severe NPDR in 155 eyes(41.7%), and proliferative DR(PDR)in 58 eyes(15.6%). Based on artificial intelligence diagnostic system, the results were as follows: NDR in 38 eyes(10.2%), PDR in 44 eyes(11.8%), others were NPDR. The sensitivity and specificity of artificial intelligence diagnostic system, compared with human expert grading, for the detection of any DR were 0.82 and 0.91, and the kappa value was 0.77(χ2=20.39, PCONCLUSION: This study shows that a deep learning-assisted diagnostic system with an artificial intelligence for grading diabetic retinopathy is a reliable alternative to diabetic retinopathy assessment, thus the use of this system may be a valuable tool in evaluating the DR.