Deep learning algorithms have been used to detect diabetic retinopathy (DR) with specialist-level accuracy. This study aims to validate one such algorithm on a large-scale clinical population, and compare the algorithm performance with that of human graders. A total of 25,326 gradable retinal images of patients with diabetes from the community-based, nationwide screening program of DR in Thailand were analyzed for DR severity and referable diabetic macular edema (DME). Grades adjudicated by a panel of international retinal specialists served as the reference standard. Relative to human graders, for detecting referable DR (moderate NPDR or worse), the deep learning algorithm had significantly higher sensitivity (0.97 vs. 0.74, p < 0.001), and a slightly lower specificity (0.96 vs. 0.98, p < 0.001). Higher sensitivity of the algorithm was also observed for each of the categories of severe or worse NPDR, PDR, and DME ( p < 0.001 for all comparisons). The quadratic-weighted kappa for determination of DR severity levels by the algorithm and human graders was 0.85 and 0.78 respectively ( p < 0.001 for the difference). Across different severity levels of DR for determining referable disease, deep learning significantly reduced the false negative rate (by 23%) at the cost of slightly higher false positive rates (2%). Deep learning algorithms may serve as a valuable tool for DR screening.
Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC-AUC of 0.89 (95% CI: 0.87-0.91), corresponding to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82-85%), but only half the specificity (45-50%, p < 0.001). Our model can also detect the presence of intraretinal fluid (AUC: 0.81; 95% CI: 0.81-0.86) and subretinal fluid (AUC 0.88; 95% CI: 0.85-0.91). Using deep learning to make predictions via simple 2D images without sophisticated 3Dimaging equipment and with better than specialist performance, has broad relevance to many other applications in medical imaging.
Objective To evaluate retinal vascular structural change in ocular Behcet's using optical coherence tomography angiography (OCTA) and fluorescein angiography (FA). Methods An analytic cross-sectional study of 37 eyes of 21 Behcet's uveitic patients was performed. Foveal retinal thickness (FRT), perifoveal hypoperfusion areas in superficial capillary plexus (SCP), and deep capillary plexus (DCP) were measured with swept-source optical coherence tomography and OCTA. FA images were used for assessing the vascular features and correlation. Results Twenty-one patients were enrolled (52.4% males). The average age at onset was 36.7 ± 12.93 years. The median of disease duration was 5 years (1–25). FRT was 118.1 ± 52.35 μm, which correlated with visual acuity (95% CI −60.47, −13.92). Using OCTA, the area of hypoperfusion in SCP (0.47 ± 0.17 mm2) was smaller than that in DCP (1.94 ± 3.87 mm2) (p < 0.001). Superficial to deep capillary plexus nonperfusion (SCP : DCP) ratio was 0.57 ± 0.27 which had the positive coefficient correlation with visual acuity (95% CI −0.644, −0.015). Conclusions OCTA is an alternative noninvasive method to monitor macular ischemia in Behcet. Behcet's uveitis affects DCP more than SCP. Decreasing SCP : DCP ratio and decrease FRT correlates with poor visual acuity. Macular ischemia and DCP loss can be found early and can explain vision loss in Behcet.
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