Using artificial intelligence reading label system in diabetic retinopathy grading training of junior ophthalmology residents and medical students
Ruoan Han, Weihong Yu, Huan Chen, Youxin Chen
Abstract
PURPOSE: Evaluate the efficiency of using an artificial intelligence reading label system in the diabetic retinopathy grading training of junior ophthalmology resident doctors and medical students. METHODS: Loading 520 diabetic retinopathy patients' colour fundus images into the artificial intelligence reading label system. Thirteen participants, including six junior ophthalmology residents and seven medical students, read the images randomly for eight rounds. They evaluated the grading of images and labeled the typical lesions. The sensitivity, specificity, and kappa scores were determined by comparison with the participants' results and diagnosis gold standards. RESULTS: Through eight rounds of reading, the average kappa score was elevated from 0.67 to 0.81. The average kappa score for rounds 1 to 4 was 0.77, and the average kappa score for rounds 5 to 8 was 0.81. The participants were divided into two groups. The participants in Group 1 were junior ophthalmology resident doctors, and the participants in Group 2 were medical students. The average kappa score of Group 1 was elevated from 0.71 to 0.76. The average kappa score of Group 2 was elevated from 0.63 to 0.84. CONCLUSION: The artificial intelligence reading label system is a valuable tool for training resident doctors and medical students in performing diabetic retinopathy grading.