Anti-VEGF reduces inflammatory features in macular edema secondary to retinal vein occlusion

AIM: To investigate the anti-inflammatory effect of intravitreal injection of anti-vascular endothelial growth factor(anti-VEGF) in patients with macular edema secondary to retinal vein occlusion(RVO-ME).METHODS: Twenty-eight eyes from twenty-eight treatment-na?ve patients(14 males and 14 females) with RVO-ME were included in this retrospective study.The retinal vein occlusion(RVO) was comprised of both central retinal vein occlusion(CRVO,n=14) and branch retinal vein occlusion(BRVO,n=14).Intravitreal injection of anti-VEGF reagents were administered monthly for three consecutive months,in which 18 patients were injected with ranibizumab and 10 patients were injected with conbercept.All eyes were imaged with optical coherence tomography angiography(OCTA) at baseline and 1wk after monthly intravitreal anti-VEGF injection.The visual acuity(VA),central macular thickness(CMT),the number of hyperreflective foci(HRF) recognized as an inflammatory sign in OCT images,and non-perfusion area(NPA),were compared before and after anti-VEGF treatments.RESULTS: The mean interval between baseline and follow-up was 29.4±0.79(range,27-48)d.Compared with the baseline,the VA improved(log MAR 1.5±0.1 vs 0.8±0.1,P<0.05) and CMT decreased(460±34.0 μm vs 268.8±12.0 μm,P<0.05),significantly,after antiVEGF treatment.The number of HRF was decreased significantly(76.5±4.8 vs 47.8±4.3,P<0.05) after antiVEGF treatment.CONCLUSION: Anti-VEGF therapy is effective in treating RVO-ME.The mechanisms for the decreased HRF and the reduction of NPA by anti-VEGF therapy merits further exploration.

A new handheld fundus camera combined with visual artificial intelligence facilitates diabetic retinopathy screening

AIM: To explore the performance in diabetic retinopathy(DR) screening of artificial intelligence(AI) system by evaluating the image quality of a handheld Optomed Aurora fundus camera in comparison to traditional tabletop fundus cameras and the diagnostic accuracy of DR of the two modalities. METHODS: Overall, 630 eyes were included from three centers and screened by a handheld camera(Aurora, Optomed, Oulu, Finland) and a table-top camera. Image quality was graded by three masked and experienced ophthalmologists. The diagnostic accuracy of the handheld camera and AI system was evaluated in assessing DR lesions and referable DR.RESULTS: Under nonmydriasis status, the handheld fundus camera had better image quality in centration, clarity, and visible range(1.47, 1.48, and 1.40) than conventional tabletop cameras(1.30, 1.28, and 1.18;P<0.001). Detection of retinal hemorrhage, hard exudation,and macular edema were comparable between the two modalities, in principle, with the area under the curve of the handheld fundus camera slightly lower. The sensitivity and specificity for the detection of referable DR with the handheld camera were 82.1%(95%CI: 72.1%-92.2%) and 97.4%(95%CI: 95.4%-99.5%), respectively. The performance of AI detection of DR using the Phoebus Algorithm was satisfactory;however, Phoebus showed a high sensitivity(88.2%, 95%CI: 79.4%-97.1%) and low specificity(40.7%, 95%CI: 34.1%-47.2%) when detecting referable DR.CONCLUSION: The handheld Aurora fundus camera combined with autonomous AI system is well-suited in DR screening without mydriasis because of its high sensitivity of DR detection as well as its image quality, but its specificity needs to be improved with better modeling of the data. Use of this new system is safe and effective in the detection of referable DR in real world practice.