Evaluation of a New Neural Network Classifier for Diabetic Retinopathy

Abstract

Background: Medical image segmentation is a well-studied subject within the field of image processing. The goal of this research is to create an AI retinal screening grading system that is both accurate and fast. We introduce a new segmentation network which achieves state-of-the-art results on semantic segmentation of color fundus photographs. By applying the net-work to identify anatomical markers of diabetic retinopathy (DR) and diabetic macular edema (DME), we collect sufficient information to classify patients by grades R0 and R1 or above, M0 and M1. Methods: The AI grading system was trained on screening data to evaluate the presence of DR and DME. The core algorithm of the system is a deep learning network that segments relevant anatomical features in a retinal image. Patients were graded according to the standard NHS Diabetic Eye Screening Program feature-based grading protocol. Results: The algorithm performance was evaluated with a series of 6,981 patient retinal images from routine diabetic eye screenings. It correctly predicted 98.9% of retinopathy events and 95.5% of maculopathy events. Non-disease events prediction rate was 68.6% for retinopathy and 81.2% for maculopathy. Conclusion: This novel deep learning model was trained and tested on patient data from annual diabetic retinopathy screenings can classify with high accuracy the DR and DME status of a person with diabetes. The system can be easily reconfigured according to any grading protocol, without running a long AI training procedure. The incorporation of the AI grading system can increase the graders’ productivity and improve the final outcome accuracy of the screening process.

Performance of the AIDRScreening system in detecting diabetic retinopathy in the fundus photographs of Chinese patients: a prospective, multicenter, clinical study

“…An AI algorithm combining fundus photographs with OCT images had a very high sensitivity (>95%) and appeared to be more effective than the deep learning model in screening DME ( 29 ). Recently, Katz et al ( 30 ) introduced a new segmentation network for fundus photographs to evaluate the presence of DME. The overall sensitivity of this classifier was 95.5% with a specificity of 81.2% and a false-positive rate of 31.4%.…”

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

“…After testing, the AUC of the model was 0.958 and the kappa score was 0.860. Katz et al (2022) constructed an AI model based on W-net, which can automatically classify DR. They collected 6,981 fundus images and used them to train and test the AI model.…”

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

“…After testing, the model's accuracy and AUC were 0.99 and 1.00, respectively, for the Kaggle dataset. Katz et al [66] created an AI classification model by the W-net. This model divides patients with DR into DR and DME groups by identifying the DR and DME markers on color fundus photographs.…”

“…Liu et al [64] No DR, mild DR, moderate DR, severe DR, PDR Fundus image EyePACS dataset, APTOS dataset, DeepDR dataset EfficientNetB4, EfficientNetB5, NASNetLarge, Xception, InceptionResNetV2 Erciyas and Barışçı [65] Classification Fundus image Kaggle dataset Convolution neural network, Transfer learning, Attention mechanism Katz et al [66] Classification Fundus image 6981 images W-net Yaqoob et al [67] No referable DME, referable DME Fundus image Messidor-2 dataset ResNet-50, Random Forest…”