This study aimed to develop an automated computer-based algorithm to estimate axial length and subfoveal choroidal thickness (SFCT) based on color fundus photographs. In the population-based Beijing Eye Study 2011, we took fundus photographs and measured SFCT by optical coherence tomography (OCT) and axial length by optical low-coherence reflectometry. Using 6394 color fundus images taken from 3468 participants, we trained and evaluated a deep-learning-based algorithm for estimation of axial length and SFCT. The algorithm had a mean absolute error (MAE) for estimating axial length and SFCT of 0.56 mm [95% confidence interval (CI): 0.53,0.61] and 49.20 μm (95% CI: 45.83,52.54), respectively. Estimated values and measured data showed coefficients of determination of r2 = 0.59 (95% CI: 0.50,0.65) for axial length and r2 = 0.62 (95% CI: 0.57,0.67) for SFCT. Bland–Altman plots revealed a mean difference in axial length and SFCT of −0.16 mm (95% CI: −1.60,1.27 mm) and of −4.40 μm (95% CI, −131.8,122.9 μm), respectively. For the estimation of axial length, heat map analysis showed that signals predominantly from overall of the macular region, the foveal region, and the extrafoveal region were used in the eyes with an axial length of < 22 mm, 22–26 mm, and > 26 mm, respectively. For the estimation of SFCT, the convolutional neural network (CNN) used mostly the central part of the macular region, the fovea or perifovea, independently of the SFCT. Our study shows that deep-learning-based algorithms may be helpful in estimating axial length and SFCT based on conventional color fundus images. They may be a further step in the semiautomatic assessment of the eye.

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Abstract. On account of the complexity and uncertainty in cyberspace competition, cyber ecosystem is brought forth to meet the requirements of cyberspace security and complex adaptive system. Herein, a novel information diffusion model is presented with the change of notes based on Susceptible-Escaped-Infected-Removed-Quarantined-Susceptible (SEIQRS). In detail, the process and mechanism of nodes increasing and decreasing are considered upon traditional epidemic and information diffusion model, and the system dynamics equations are derived. Via the criterion of Routh-Hurwitz stability, the stability conditions are further investigated, as well as the corresponding requirements. Theoretical research and simulations demonstrate that in an open cyber ecosystem the proposed information diffusion model can be well controlled by modulating the number of nodes in the case of node increasing and decreasing.

This paper reviews and demonstrates how to use digital technology to design the ecological environment. Through the analysis and summary of current research fields and status quo of eco-design, we explain the development of ecosystem design in life and building, and propose the key issues to be addressed, discuss the inadequacies of current research, and predict the next main research tendency of ecosystem design based on digital technology.

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