AIMC Topic: Humans

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Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging.

European journal of nuclear medicine and molecular imaging
PURPOSE: Tendency is to moderate the injected activity and/or reduce acquisition time in PET examinations to minimize potential radiation hazards and increase patient comfort. This work aims to assess the performance of regular full-dose (FD) synthes...

Contributions and limitations of using machine learning to predict noise-induced hearing loss.

International archives of occupational and environmental health
PURPOSE: Noise-induced hearing loss (NIHL) is a global issue that impacts people's life and health. The current review aims to clarify the contributions and limitations of applying machine learning (ML) to predict NIHL by analyzing the performance of...

Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time...

High resolution automated labeling of the hippocampus and amygdala using a 3D convolutional neural network trained on whole brain 700 μm isotropic 7T MP2RAGE MRI.

Human brain mapping
Image labeling using convolutional neural networks (CNNs) are a template-free alternative to traditional morphometric techniques. We trained a 3D deep CNN to label the hippocampus and amygdala on whole brain 700 μm isotropic 3D MP2RAGE MRI acquired a...

Systematic review of guidelines to identify recommendations for upper limb robotic rehabilitation after stroke.

European journal of physical and rehabilitation medicine
INTRODUCTION: Upper limb motor impairment is one of the most frequent stroke consequences. Robot therapy may represent a valid option for upper limb stroke rehabilitation, but there are still gaps between research evidence and their use in clinical p...

Factors that influence parents' intentions of using autonomous vehicles to transport children to and from school.

Accident; analysis and prevention
High-level autonomous vehicles (AVs) are likely to improve the quality of children's travel to and from school (such as improve travel safety and increase travel mobility). These expected benefits will not be presented if parents are not willing to u...

Accelerating Drug Design against Novel Proteins Using Deep Learning.

Journal of chemical information and modeling
In the world plagued by the emergence of new diseases, it is essential that we accelerate the drug design process to develop new therapeutics against them. In recent years, deep learning-based methods have shown some success in ligand-based drug desi...

Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients.

Nature cancer
Cell-line screens create expansive datasets for learning predictive markers of drug response, but these models do not readily translate to the clinic with its diverse contexts and limited data. In the present study, we apply a recently developed tech...

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.

Medical & biological engineering & computing
Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insufficient data. Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network...

Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition.

Sensors (Basel, Switzerland)
With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis d...