AIMC Topic: Humans

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Effects of Robot-Assisted Gait Training in Patients With Multiple Sclerosis: A Single-Blinded Randomized Controlled Study.

American journal of physical medicine & rehabilitation
OBJECTIVE: This study aims to evaluate and compare the effects of conventional and robot-assisted gait training (RAGT) programs on fatigue, mood, and quality of life in patients with multiple sclerosis who have fatigue.

Intelligent type 2 diabetes risk prediction from administrative claim data.

Informatics for health & social care
Type 2 diabetes is a chronic, costly disease and is a serious global population health problem. Yet, the disease is well manageable and preventable if there is an early warning. This study aims to apply supervised machine learning algorithms for deve...

A muggles guide to deep learning wizardry.

Radiography (London, England : 1995)
OBJECTIVES: Growing interest in the applications of artificial intelligence (AI) and, in particular, deep learning (DL) in nuclear medicine and radiology partitions the professional community. At one end of the spectrum are our expert DL wizards deve...

Line-field confocal optical coherence tomography as a tool for three-dimensional in vivo quantification of healthy epidermis: A pilot study.

Journal of biophotonics
Epidermal three-dimensional (3D) topography/quantification has not been completely characterized yet. The recently developed line-field confocal optical coherence tomography (LC-OCT) provides real-time, high-resolution, in-vivo 3D imaging of the skin...

Zenithal isotropic object counting by localization using adversarial training.

Neural networks : the official journal of the International Neural Network Society
Counting objects in images is a very time-consuming task for humans that yields to errors caused by repetitiveness and boredom. In this paper, we present a novel object counting method that, unlike most of the recent works that focus on the regressio...

RA-GCN: Graph convolutional network for disease prediction problems with imbalanced data.

Medical image analysis
Disease prediction is a well-known classification problem in medical applications. Graph Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients' features relative to each other. This can be achieved by modeling the problem a...

Curriculum learning for improved femur fracture classification: Scheduling data with prior knowledge and uncertainty.

Medical image analysis
An adequate classification of proximal femur fractures from X-ray images is crucial for the treatment choice and the patients' clinical outcome. We rely on the commonly used AO system, which describes a hierarchical knowledge tree classifying the ima...

Brain oscillatory correlates of visuomotor adaptive learning.

NeuroImage
Sensorimotor adaptation involves the recalibration of the mapping between motor command and sensory feedback in response to movement errors. Although adaptation operates within individual movements on a trial-to-trial basis, it can also undergo learn...

Causal decoding of individual cortical excitability states.

NeuroImage
Brain responsiveness to stimulation fluctuates with rapidly shifting cortical excitability state, as reflected by oscillations in the electroencephalogram (EEG). For example, the amplitude of motor-evoked potentials (MEPs) elicited by transcranial ma...

Comparing deep learning-based automatic segmentation of breast masses to expert interobserver variability in ultrasound imaging.

Computers in biology and medicine
Deep learning is a powerful tool that became practical in 2008, harnessing the power of Graphic Processing Unites, and has developed rapidly in image, video, and natural language processing. There are ongoing developments in the application of deep l...