Learning from healthy and stable eyes

TL;DR: The machine learning system described here can accurately differentiate between healthy and glaucomatous subjects based on their extracted images from OCT data and color fundus images, which should help to improve the diagnostic accuracy inglaucoma.

TL;DR: Deep learning algorithms have the potential to significantly improve diagnostic capabilities in glaucoma, but their application in clinical practice requires careful validation, with consideration of the target population, the reference standards used to build the models, and potential sources of bias.

TL;DR: The clinical utility of OCT with respect to diagnosis and progression monitoring is examined, with additional emphasis on advances in OCT technology that continue to facilitate glaucoma research and inform clinical management strategies.

TL;DR: Ganglion cell–inner plexiform layer and 3D volume BKDS show promise for identifying change in severely advanced glaucoma and suggest that structural change can be detected in very advanced disease.

TL;DR: Combinations involving trigonometric and hyperbolic functions and power 5 Indefinite Integrals of Special Functions 6 Definite Integral Integral Functions 7.Associated Legendre Functions 8 Special Functions 9 Hypergeometric Functions 10 Vector Field Theory 11 Algebraic Inequalities 12 Integral Inequality 13 Matrices and related results 14 Determinants 15 Norms 16 Ordinary differential equations 17 Fourier, Laplace, and Mellin Transforms 18 The z-transform

Abstract: Introduction. Elementary Functions. Indefinite Integrals of Elementary Functions. Definite Integrals of Elementary Functions. Indefinite Integrals of Special Functions. Definite Integrals of Special Functions. Special Functions. Vector Field Theory. Algebraic Inequalities. Integral Inequalities. Matrices and Related Results. Determinants. Norms. Ordinary Differential Equations. Fourier, Laplace, and Mellin Transforms. Bibliographic References. Classified Supplementary References.

TL;DR: In this paper, the estimation and testing of long-run relations in economic modeling are addressed, starting with a vector autoregressive (VAR) model, the hypothesis of cointegration is formulated as a hypothesis of reduced rank of the long run impact matrix.

Abstract: This paper gives a systematic application of maximum likelihood inference concerning cointegration vectors in non-stationary vector valued autoregressive time series models with Gaussian errors, where the model includes a constant term and seasonal dummies. The hypothesis of cointegration is given a simple parametric form in terms of cointegration vectors and their weights. The relation between the constant term and a linear trend in the non-stationary part of the process is discussed and related to the weights. Tests for the presence of cointegration vectors, both with and without a linear trend in the non-stationary part of the process are derived. Then estimates and tests under linear restrictions on the cointegration vectors and their weights are given. The methods are illustrated by data from the Danish and the Finnish economy on the demand for money. Copyright 1990 by Blackwell Publishing Ltd