Development of an artificial intelligent grading diagnosis model for diabetic fundus lesions based on EasyDL and its verification evaluation

Objective To innovatively utilize the open artificial intelligence （AI） platform EasyDL to independently develop an AI auxiliary diagnosis model for diabetic retinopathy （DR）, and evaluate its diagnostic accuracy indicators. Methods 35 126 fundus photos of the diabetes fundus disease data set published by Kaggle were used as the training set, and uploaded to the EasyDL open platform to establish an AI auxiliary diagnosis model. A total of 300 color fundus photographs of bilateral eyes of 150 patients with diabetes mellitus who received clinical DR screening were collected as the test set. The diagnosis of 3 ophthalmologists with deputy director title or above was considered as the gold standard. The diagnostic accuracy for the grading of DR by the AI diagnosis model, junior physicians, intermediate physicians and these combined was evaluated, respectively. Results There were 170 patients with non-DR （NDR） and mild non-proliferative DR （NPDR）, and 130 patients with moderate and severe NPDR and proliferative DR