of a deep learning
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.
Abstract
誗 AIM : To evaluate a deep learning - assisted diagnostic system with an artificial intelligence for the detection of diabetic retinopathy ( DR ) . 誗 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. 誗 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 ( 字 2 = 20 . 39 , P < 0郾 05 ) . 誗 CONCLUSION : 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.