Medical & biological engineering & computing
Nov 18, 2017
Impedance cardiography is a low-cost noninvasive technique, based on monitoring of the thoracic impedance, for estimation of stroke volume (SV). Impedance cardiogram (ICG) is the negative of the first derivative of the impedance signal. A technique f...
Considering the pathologic importance of metabolic disturbances, advanced glycation end products (AGEs), and chronic inflammation in diabetes mellitus and ameliorating potentials of l-carnosine in hampering these detritions and because these effects ...
This study investigates the comprehension of wh-questions in individuals with aphasia (IWA) speaking Turkish, a non-wh-movement language, and German, a wh-movement language. We examined six German-speaking and 11 Turkish-speaking IWA using picture-po...
The human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network w...
BACKGROUND: The purpose of this cadaveric study was to determine the efficacy of a flexible, next-generation robotic surgical system for transoral robotic hypopharyngectomy.
BACKGROUND AND OBJECTIVE: Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual...
Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the 'knowledge' learned from non-medical imag...
BACKGROUND: Accurate assessment of kidney function is clinically important, but estimates of glomerular filtration rate (GFR) by regression are imprecise.
PURPOSE: To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning.
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