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In Eye and vision (London, England)

Background : The goal was to characterize retinal vasculature by quantitative analysis of arteriole-to-venule (A/V) ratio and vessel density in fundus photos taken with the PanOptic iExaminer System.

Methods : The PanOptic ophthalmoscope equipped with a smartphone was used to acquire fundus photos centered on the optic nerve head. Two fundus photos of a total of 19 eyes from 10 subjects were imaged. Retinal vessels were analyzed to obtain the A/V ratio. In addition, the vessel tree was extracted using deep learning U-NET, and vessel density was processed by the percentage of pixels within vessels over the entire image.

Results : All images were successfully processed for the A/V ratio and vessel density. There was no significant difference of averaged A/V ratio between the first (0.77 ± 0.09) and second (0.77 ± 0.10) measurements (P = 0.53). There was no significant difference of averaged vessel density (%) between the first (6.11 ± 1.39) and second (6.12 ± 1.40) measurements (P = 0.85).

Conclusions : Quantitative analysis of the retinal vasculature was feasible in fundus photos taken using the PanOptic ophthalmoscope. The device appears to provide sufficient image quality for analyzing A/V ratio and vessel density with the benefit of portability, easy data transferring, and low cost of the device, which could be used for pre-clinical screening of systemic, cerebral and ocular diseases.

Hu Huiling, Wei Haicheng, Xiao Mingxia, Jiang Liqiong, Wang Huijuan, Jiang Hong, Rundek Tatjana, Wang Jianhua

2020

Arteriovenous ratio, Deep learning, Image analysis, Retina, Smartphone ophthalmoscope, Vessel density