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Surgical skill levels: Classification and analysis using deep neural network model and motion signals.
Computer methods and programs in biomedicine
May 13, 2019
BACKGROUND AND OBJECTIVES: Currently, the assessment of surgical skills relies primarily on the observations of expert surgeons. This may be time-consuming, non-scalable, inconsistent and subjective. Therefore, an automated system that can objectivel...
A semi-blind online dictionary learning approach for fMRI data.
Journal of neuroscience methods
May 11, 2019
BACKGROUND: Online dictionary learning (ODL) has been applied to extract brain networks from functional magnetic resonance imaging (fMRI) data in recent year. Moreover, the supervised dictionary learning (SDL) that fixed the task stimulus curves as p...
Segmenting brain tumors from FLAIR MRI using fully convolutional neural networks.
Computer methods and programs in biomedicine
May 11, 2019
BACKGROUND AND OBJECTIVE: Magnetic resonance imaging (MRI) is an indispensable tool in diagnosing brain-tumor patients. Automated tumor segmentation is being widely researched to accelerate the MRI analysis and allow clinicians to precisely plan trea...
Psychosocial Health Interventions by Social Robots: Systematic Review of Randomized Controlled Trials.
Journal of medical Internet research
May 10, 2019
BACKGROUND: Social robots that can communicate and interact with people offer exciting opportunities for improved health care access and outcomes. However, evidence from randomized controlled trials (RCTs) on health or well-being outcomes has not yet...
Classifying intracranial stenosis disease severity from functional MRI data using machine learning.
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
May 8, 2019
Translation of many non-invasive hemodynamic MRI methods to cerebrovascular disease patients has been hampered by well-known artifacts associated with delayed blood arrival times and reduced microvascular compliance. Using machine learning and suppor...
Statistical Approaches Based on Deep Learning Regression for Verification of Normality of Blood Pressure Estimates.
Sensors (Basel, Switzerland)
May 8, 2019
Oscillometric blood pressure (BP) monitors currently estimate a single point but do not identify variations in response to physiological characteristics. In this paper, to analyze BP's normality based on oscillometric measurements, we use statistical...
Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants.
NeuroImage
May 7, 2019
Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, however, creating a need for automated segmentation m...
A Convolutional Neural Network for the Detection of Asynchronous Steady State Motion Visual Evoked Potential.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 7, 2019
A key issue in brain-computer interface (BCI) is the detection of intentional control (IC) states and non-intentional control (NC) states in an asynchronous manner. Furthermore, for steady-state visual evoked potential (SSVEP) BCI systems, multiple s...
Fuzzy Logic Type-2 Based Wireless Indoor Localization System for Navigation of Visually Impaired People in Buildings.
Sensors (Basel, Switzerland)
May 7, 2019
The ability to precisely locate and navigate a partially impaired or a blind person within a building is increasingly important for a wide variety of public safety and localization services. In this paper, we explore indoor localization algorithms us...
Impact of the rise of artificial intelligence in radiology: What do radiologists think?
Diagnostic and interventional imaging
May 6, 2019
PURPOSE: The purpose of this study was to assess the perception, knowledge, wishes and expectations of a sample of French radiologists towards the rise of artificial intelligence (AI) in radiology.
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