AIMC Topic: Healthy Volunteers
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Quantification of intravoxel incoherent motion with optimized b-values using deep neural network.
Magnetic resonance in medicine
Feb 16, 2021
PURPOSE: To develop a framework for quantifying intravoxel incoherent motion (IVIM) parameters, where a neural network for quantification and b-values for diffusion-weighted imaging are simultaneously optimized.
Neural network enhanced 3D turbo spin echo for MR intracranial vessel wall imaging.
Magnetic resonance imaging
Feb 4, 2021
PURPOSE: To improve the signal-to-noise ratio (SNR) and image sharpness for whole brain isotropic 0.5 mm three-dimensional (3D) T weighted (Tw) turbo spin echo (TSE) intracranial vessel wall imaging (IVWI) at 3 T.
Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach.
Scientific reports
Jan 13, 2021
The purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device's built-in softwa...
Humans
Case-Control Studies
Deep Learning
Tomography, Optical Coherence
Healthy Volunteers
Aged
Diabetic Retinopathy
Angiography
Image Interpretation, Computer-Assisted
Adult
Neural Networks, Computer
Diagnostic Techniques, Ophthalmological
Fovea Centralis
Male
Retrospective Studies
Middle Aged
Female
Artificial Intelligence-Assisted motion capture for medical applications: a comparative study between markerless and passive marker motion capture.
Computer methods in biomechanics and biomedical engineering
Dec 8, 2020
We aimed to determine whether artificial intelligence (AI)-assisted markerless motion capture software is useful in the clinical medicine and rehabilitation fields. Currently, it is unclear whether the AI-assisted markerless method can be applied to ...
EEG-based trial-by-trial texture classification during active touch.
Scientific reports
Nov 27, 2020
Trial-by-trial texture classification analysis and identifying salient texture related EEG features during active touch that are minimally influenced by movement type and frequency conditions are the main contributions of this work. A total of twelve...
Development of LC-MS/MS determination method and backpropagation artificial neural networks pharmacokinetic model of febuxostat in healthy subjects.
Journal of clinical pharmacy and therapeutics
Nov 17, 2020
WHAT IS KNOWN AND OBJECTIVE: Febuxostat is a well-known drug for treating hyperuricemia and gout. The published methods for determination of febuxostat in human plasma might be unsuitable for high-throughput determination and widespread application. ...
Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices.
Scientific reports
Nov 12, 2020
Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. How...
Multi-band MR fingerprinting (MRF) ASL imaging using artificial-neural-network trained with high-fidelity experimental data.
Magnetic resonance in medicine
Oct 26, 2020
PURPOSE: We aim to leverage the power of deep-learning with high-fidelity training data to improve the reliability and processing speed of hemodynamic mapping with MR fingerprinting (MRF) arterial spin labeling (ASL).
Wearable Biofeedback Improves Human-Robot Compliance during Ankle-Foot Exoskeleton-Assisted Gait Training: A Pre-Post Controlled Study in Healthy Participants.
Sensors (Basel, Switzerland)
Oct 17, 2020
The adjunctive use of biofeedback systems with exoskeletons may accelerate post-stroke gait rehabilitation. Wearable patient-oriented human-robot interaction-based biofeedback is proposed to improve patient-exoskeleton compliance regarding the intera...
Reconstructing lost BOLD signal in individual participants using deep machine learning.
Nature communications
Oct 7, 2020
Signal loss in blood oxygen level-dependent (BOLD) functional neuroimaging is common and can lead to misinterpretation of findings. Here, we reconstructed compromised fMRI signal using deep machine learning. We trained a model to learn principles gov...
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