Purpose: To evaluate the screening potential of a deep learning algorithm-derived severity score by determining its ability to detect clinically significant severe retinopathy of prematurity (ROP).Methods: Fundus photographs were collected, and standard panel diagnosis was generated for each examination by combining three independent image-based gradings. All images were analyzed using a deep learning algorithm, and a quantitative assessment of retinal vascular abnormality (DeepROP score) was assigned on a 1 to 100 scale. The area under the receiver operating curve and distribution pattern of all diagnostic parameters and categories of ROP were analyzed. The correlation between the DeepROP score and expert rank ordering according to overall ROP severity of 50 examinations was calculated.Results: A total of 9,882 individual examinations with 54,626 images from 2,801 infants were analyzed. Fifty-six examinations (0.6%) demonstrated Type 1 ROP and 54 examinations (0.5%) demonstrated Type 2 ROP. The DeepROP score had an area under the receiver operating curve of 0.981 for detecting Type 1 ROP and 0.986 for Type 2 ROP. There was a statistically significant correlation between the expert rank ordering of overall disease severity and the DeepROP score (correlation coefficient 0.758, P , 0.001). When hypothetical referral cutoff score of 35 was selected, all cases of severe ROP (Type 1 and Type 2 ROP) was captured and 8,562 eyes (87.6%) with no or mild ROP were excluded. Conclusion:The DeepROP score determined by deep learning algorithm was an objective and quantitative indicator for the severity of ROP, and it had potential in automated detecting clinically significant severe ROP.

IntroductionThe study aimed to determine the effect of the scanning area used for high-speed ultra-widefield swept-source optical coherence tomography angiography (SS-OCTA) on the detection rate of diabetic retinopathy (DR) lesions.MethodsThis prospective, observational study involved diabetic patients between October 2021 and April 2022. The participants underwent a comprehensive ophthalmic examination and high-speed ultra-widefield SS-OCTA using a 24 mm × 20 mm scanning protocol. A central area denoted as “12 mm × 12 mm-central” was extracted from the 24 mm × 20 mm image, and the remaining area was denoted as “12 mm~24mm-annulus.” The rates of detection of DR lesions using the two scanning areas were recorded and compared.ResultsIn total, 172 eyes (41 eyes with diabetes mellitus without DR, 40 eyes with mild to moderate non-proliferative diabetic retinopathy (NPDR), 51 eyes with severe NPDR, and 40 eyes with proliferative diabetic retinopathy (PDR) from 101 participants were included. The detection rates of microaneurysms (MAs), intraretinal microvascular abnormalities (IRMAs), and neovascularization (NV) for the 12 mm × 12 mm central and 24 mm × 20 mm images were comparable (p > 0.05). The detection rate of NPAs for the 24 mm × 20 mm image was 64.5%, which was significantly higher than that for the 12 mm × 12 mm central image (52.3%, p < 0.05). The average ischemic index (ISI) was 15.26% for the 12 mm~24mm-annulus, which was significantly higher than that for the 12 mm × 12 mm central image (5.62%). Six eyes had NV and 10 eyes had IRMAs that only existed in the 12 mm~24mm-annulus area.ConclusionsThe newly developed high-speed ultra-widefield SS-OCTA can capture a 24 mm × 20 mm retinal vascular image during a single scan, which improves the accuracy of detecting the degree of retinal ischemia and detection rate of NV and IRMAs.