Translational vision science & technology
Nov 1, 2021
PURPOSE: To investigate whether a correction based on a Humphrey field analyzer (HFA) 24-2/30-2 visual field (VF) can improve the prediction performance of a deep learning model to predict the HFA 10-2 VF test from macular optical coherence tomograph...
Water science and technology : a journal of the International Association on Water Pollution Research
Nov 1, 2021
Rain garden is effective in reducing storm water runoff, whose efficiency depends upon several parameters such as soil type, vegetation and meteorological factors. Evaluation of rain gardens has been done by various researchers. However, knowledge fo...
Translational vision science & technology
Nov 1, 2021
PURPOSE: To build and evaluate deep learning models for recognizing cataract surgical steps from whole-length surgical videos with minimal preprocessing, including identification of routine and complex steps.
Translational vision science & technology
Nov 1, 2021
PURPOSE: To evaluate the clinical performance of an automated diabetic retinopathy (DR) screening model to detect referable cases at Siriraj Hospital, Bangkok, Thailand.
Translational vision science & technology
Nov 1, 2021
PURPOSE: We propose a deep learning-based image reconstruction algorithm to produce high-resolution optical coherence tomographic angiograms (OCTA) of the intermediate capillary plexus (ICP) and deep capillary plexus (DCP).
Translational vision science & technology
Nov 1, 2021
PURPOSE: The purpose of this study was to engineer deep learning (DL) models that can identify myopic maculopathy in patients with high myopia based on optical coherence tomography (OCT) images.
Translational vision science & technology
Nov 1, 2021
PURPOSE: The purpose of this study was to analyze the choroidal sublayer morphologic features in emmetropic and myopic children using an automatic segmentation model, and to explore the relationship between choroidal sublayers and spherical equivalen...
Machine learning is an important tool for extracting information from medical images. Deep learning has made this more efficient by not requiring an explicit feature extraction step and in some cases detecting features that humans had not identified....
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