Effect of Conbercept Treatment on Macular Edema and Microvascular Structure in Eyes with Retinal Vein Occlusions

Changes in wider field swept-source OCT angiography vascular metrics with anti-vascular endothelial growth factor therapy in central retinal vein occlusion

PURPOSE

To investigate the impact of anti-VEGF therapy on vascular metrics in eyes with macular edema secondary to central retinal vein occlusion (CRVO) using wider field swept-source OCT angiography (WF SS-OCTA).

METHODS

We included 23 eyes with macular edema associated with non-ischemic CRVO from 22 patients treated with anti-VEGF therapy (median number of injections: 5 [2-9]). Changes in vessel density (VD), vessel skeletonized density (VSD), and foveal avascular zone (FAZ) parameters were measured using WF SS-OCTA. Visual acuity (VA) and central subfield thickness (CST) were also measured.

RESULTS

Median CST decreased significantly from 369 µm (305-531) to 267 µm (243-300, p < 0.001). VD and VSD parameters in 12 × 12 mm images showed significant reductions. For instance, VSD in the whole retina decreased from a median of 13.37 (11.22-13.74) to 11.29 (9.36-12.97, p = 0.013). Additionally, a significant increase in FAZ circularity was found, suggesting improved microvascular integrity. Significant inverse correlations were found between the number of anti-VEGF injections and all VSD and VD parameters on the 12 × 12 mm images (p < 0.05). Notably, the reductions in VSD and VD on 12 × 12 mm angiograms in the deep capillary plexus (DCP) after each injection significantly correlated with increased logMAR VA (worse VA).

CONCLUSION

Anti-VEGF therapy in CRVO patients not only mitigates macular edema but also alters the overall microvascular morphology and functionality as revealed by WF SS-OCTA.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

AIM

To develop an artificial intelligence (AI) diagnosis model based on deep learning (DL) algorithm to diagnose different types of retinal vein occlusion (RVO) by recognizing color fundus photographs (CFPs).

METHODS

Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets, and used to train, verify and test the diagnostic model of RVO. All the images were divided into four categories [normal, central retinal vein occlusion (CRVO), branch retinal vein occlusion (BRVO), and macular retinal vein occlusion (MRVO)] by three fundus disease experts. Swin Transformer was used to build the RVO diagnosis model, and different types of RVO diagnosis experiments were conducted. The model's performance was compared to that of the experts.

RESULTS

The accuracy of the model in the diagnosis of normal, CRVO, BRVO, and MRVO reached 1.000, 0.978, 0.957, and 0.978; the specificity reached 1.000, 0.986, 0.982, and 0.976; the sensitivity reached 1.000, 0.955, 0.917, and 1.000; the F1-Sore reached 1.000, 0.955 0.943, and 0.887 respectively. In addition, the area under curve of normal, CRVO, BRVO, and MRVO diagnosed by the diagnostic model were 1.000, 0.900, 0.959 and 0.970, respectively. The diagnostic results were highly consistent with those of fundus disease experts, and the diagnostic performance was superior.

CONCLUSION

The diagnostic model developed in this study can well diagnose different types of RVO, effectively relieve the work pressure of clinicians, and provide help for the follow-up clinical diagnosis and treatment of RVO patients.