AIM:To evaluate the application value of artificial intelligence diagnosis system for fundus disease screening based on deep learning.

METHODS:A total of 1 345 patients(2 690 eyes)in our hospital were recruited from July 2018 to December 2018. The accuracy, specificity, consistency and sensitivity of the artificial intelligence diagnosis system were determined by comparison with ophthalmologist diagnosis and artificial intelligence diagnosis system which based on multi-layer deep convolution neural network learning.

RESULTS:The accuracy of artificial intelligence diagnosis system is 62.82%. There are 1-5(1.38±0.67)diagnoses among the patients, among which the accuracy of one diagnosis is 56.09%, the accuracy of two diagnosis is 77.96%, the accuracy of three diagnosis is 84.61%, the accuracy of four diagnosis is 86.95%, and the accuracy of five diagnosis is 60.00%; The consistency kappa value without obvious abnormality and leopard pattern fundus was 0.044 and 0.169 respectively. The sensitivity was 3.00% and 99.6% respectively, the specificity was 99.7% and 14.2% respectively. The consistency Kappa value of other diagnosis was as high as 0.57-1.00, the sensitivity was as high as 65.1%-100%, and the specificity was as high as 93.0%-100%.

CONCLUSION:This study shows that the artificial intelligence diagnosis system based on multi-layer deep convolution neural network learning is a reliable alternative to diagnose retina diseases, and it is expected to become an effective screening tool for primary medical treatment.