Screening and evaluation of diabetic retinopathy via a deep learning network model: A prospective study.

Background: Diabetic retinopathy (DR) is one of the most common serious complications in diabetic patients, and early screening and diagnosis are essential to prevent visual impairment. With the rapid development of deep learning technology, network models based on attention mechanisms have shown significant advantages in medical image analysis, which can improve the accuracy and efficiency of screening.

Aim: To evaluate the efficacy of an attention mechanism-based deep learning network model in screening for DR in natural and diabetic populations, as well as in screening with unilateral and bilateral fundus photography.

Methods: From January 2023 to June 2024, a stratified multistage cluster sampling method was adopted to select a representative sample of permanent residents aged 18-70 years from our hospital. A total of 948 fundus images from 474 participants were included in the "deep learning model" system for scoring. The fundus images were graded via the early treatment of DR [Early Treatment Diabetic Retinopathy Study (ETDRS)] scoring system as the gold standard for the diagnosis of DR. With "DR to be referred (ETDRS > 31)" as the reference variable, a receiver operating characteristic curve was drawn to evaluate the area under the curve (AUC), sensitivity and specificity of the "deep learning model" to determine the screening efficiency of the system.

Results: For each subject, in the natural population, the AUC of using the "deep learning model system" to screen "DR-requiring referral" was 0.941, and the sensitivity and specificity were 98.15% and 90.08%, respectively. The sensitivity and specificity of two-directional fundus photography were 100% and 86.91%, respectively. In the diabetic population, the AUC, sensitivity and specificity were 0.901, 98.08% and 82.10%, respectively, when "wise eye sugar net" unilateral fundus photography was used to screen for "DR-requiring referrals".

Conclusion: In both the natural population and the diabetic population, the deep learning model system has shown high sensitivity and specificity and can be used as an auxiliary means of DR screening.