PurposeThis study investigated the association of retinal fractal dimension (FD) and blood vessel tortuosity (BVT) with renal function [assessed by estimated glomerular filtrate rate (eGFR)] in healthy Chinese adults using swept-source optical coherence tomographic angiography (SS-OCTA).Materials and methodsThis cross-sectional study was conducted among ocular treatment–naïve healthy participants from Guangzhou, China. FD and BVT in the superficial capillary plexus and deep capillary plexus were measured by SS-OCTA with a 3 × 3 macula model. eGFR was calculated using the Xiangya equation, and impaired renal function (IRF) was defined as eGFR = 90 mL/min/1.73 m2. Linear regression was performed to evaluate the relationships between SS-OCTA metrics and renal function.ResultsA total of 729 participants with a mean age of 57.6 ± 9.1 years were included in the final analysis. Compared to participants with normal renal function, those with IRF had lower FD both in the superficial capillary plexus (1.658 ± 0.029 vs. 1.666 ± 0.024, p = 0.001) and deep capillary plexus (1.741 ± 0.016 vs. 1.746 ± 0.016, p = 0.0003), while the deep BVT was larger in participants with IRF than those with normal renal function (1.007 ± 0.002 vs. 1.006 ± 0.002, p = 0.028). The superficial FD was linearly and positively associated with eGFR after adjusting for confounders (β = 0.2257; 95% CI 0.0829–0.3685; p = 0.002), while BVT was not associated with eGFR (all p ≥ 0.05).ConclusionThe patients with IRF had lower FD and larger BVT than those with normal renal function. The superficial FD decreased linearly with renal function deterioration. Our study suggests that the retinal microvasculature can represent a useful indicator of subclinical renal microvascular abnormalities and serve as a useful non-invasive assessment to predict and monitor the progression of renal function.
Improving the level of autonomy during the landing phase helps promote the full-envelope autonomous flight capability of unmanned aerial vehicles (UAVs). Aiming at the identification of potential landing sites, an end-to-end state estimation method for the autonomous landing of carrier-based UAVs based on monocular vision is proposed in this paper, which allows them to discover landing sites in flight by using equipped optical sensors and avoid a crash or damage during normal and emergency landings. This scheme aims to solve two problems: the requirement of accuracy for runway detection and the requirement of precision for UAV state estimation. First, we design a robust runway detection framework on the basis of YOLOv5 (you only look once, ver. 5) with four modules: a data augmentation layer, a feature extraction layer, a feature aggregation layer and a target prediction layer. Then, the corner prediction method based on geometric features is introduced into the prediction model of the detection framework, which enables the landing field prediction to more precisely fit the runway appearance. In simulation experiments, we developed datasets applied to carrier-based UAV landing simulations based on monocular vision. In addition, our method was implemented with help of the PyTorch deep learning tool, which supports the dynamic and efficient construction of a detection network. Results showed that the proposed method achieved a higher precision and better performance on state estimation during carrier-based UAV landings.
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