Abstract
According to the latest data from the Bureau of Disease Control and Prevention of the National Health and Family Planning Commission, China currently has 199.6 million diabetic patients and has become the world's largest country with diabetes. The prevalence rate is as high as 14.3%, which is much higher than the world average of 5.8%. The primary-level ophthalmic screening service is one of the important tasks to improve primary-level medical services, and the corresponding ophthalmic imaging diagnosis technology is an important support for primary-level medical and health services. Therefore, it is very necessary for us to study the application of artificial intelligence image recognition technology for diabetic retinopathy under the medical consortium mode and to study the precise initial diagnosis, precise referral, and precise follow-up of diabetic retina under the medical conjoined mode, so as to better promote the transformation of the ophthalmology primary service model. Based on this background, in this article, we have proposed and carried out the following solution: (1) diabetes data collation. Based on medical artificial intelligence technology, this paper collected 2,265 electronic medical records from an eye hospital in Ningbo and selected 2,000 qualified medical records for data integration and preprocessing. The contents of electronic medical records mainly include age, gender, and examination records. (2) Establish diabetic retinopathy diagnosis model based on neural network algorithm. This article first uses the classic algorithm of BP neural network for modeling, chooses the Levenberg-Marquardt method as the training function, and selects 10 hidden layer units through comparison experiments. After that, ophthalmologists assessed 80 sets of test results and determined the right diagnosis rate. Finally, this article compares and analyzes the accuracy of the two routes in 80 tests.