Published online Dec 15, 2024. doi: 10.4239/wjd.v15.i12.2302
Revised: September 11, 2024
Accepted: October 21, 2024
Published online: December 15, 2024
Processing time: 106 Days and 22.7 Hours
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 scree
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.
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.
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".
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.
Core Tip: Application value of a deep learning network model based on an attention mechanism in screening diabetic retinopathy (DR). By building and optimizing a deep learning model that incorporates attention mechanisms, we analyze its accuracy and efficiency in identifying features of DR and compare it with those of traditional methods. Research has focused on key performance indicators such as the sensitivity, specificity and accuracy of the model, as well as the contribution of attention mechanisms in the feature extraction process. Ultimately, the aim is to provide a more effective screening tool to improve the accuracy of clinical diagnosis and the possibility of early intervention.