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Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism.

Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia's neural-biom...

Immediate post-operative PDE5i therapy improves early erectile function outcomes after robot assisted radical prostatectomy (RARP).

To assess whether the timing of post-operative Phosphodiesterase Inhibitor (PDE5i) therapy after Robot-Assisted Radical Prostatectomy (RARP) is associated with a change in early erectile function (EF) outcomes, continence or safety outcomes. Data were prospectively collected from a single surgeon in one tertiary centre. 158 patients were treated with PDE5i therapy post RARP over a 2-year period. P...

Effect of assist-as-needed robotic gait training on the gait pattern post stroke: a randomized controlled trial.

BACKGROUND: Regaining gait capacity is an important rehabilitation goal post stroke. Compared to clinically available robotic gait trainers, robots wi...

Dynamically learned PSO based neighborhood influenced fuzzy c-means for pre-treatment and post-treatment organ segmentation from CT images.

BACKGROUND AND OBJECTIVE: The accurate segmentation of pre-treatment and post-treatment organs is always perceived as a challenging task in medical im...

Validating Intelligent Automation Systems in Pharmacovigilance: Insights from Good Manufacturing Practices.

Pharmacovigilance is the science of monitoring the effects of medicinal products to identify and evaluate potential adverse reactions and provide nece...

Leveraging digital media data for pharmacovigilance.

The development of novel drugs in response to changing clinical requirements is a complex and costly method with uncertain outcomes. Postmarket pharma...

Artificial Intelligence in Pharmacovigilance: Scoping Points to Consider.

Artificial intelligence (AI), a highly interdisciplinary science, is an increasing presence in pharmacovigilance (PV). A better understanding of the s...

Machine learning and statistics to qualify environments through multi-traits in Coffea arabica.

Several factors such as genotype, environment, and post-harvest processing can affect the responses of important traits in the coffee production chain...

Identification of Children at Risk of Schizophrenia via Deep Learning and EEG Responses.

The prospective identification of children likely to develop schizophrenia is a vital tool to support early interventions that can mitigate the risk o...

Learning Spatiotemporal Features for Esophageal Abnormality Detection From Endoscopic Videos.

Esophageal cancer is categorized as a type of disease with a high mortality rate. Early detection of esophageal abnormalities (i.e. precancerous and e...

Comparison of deep learning synthesis of synthetic CTs using clinical MRI inputs.

There has been substantial interest in developing techniques for synthesizing CT-like images from MRI inputs, with important applications in simultane...

Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture.

Mindfulness training is associated with improvements in psychological wellbeing and cognition, yet the specific underlying neurophysiological mechanis...

Development of a Self-Harm Monitoring System for Victoria.

The prevention of suicide and suicide-related behaviour are key policy priorities in Australia and internationally. The World Health Organization has ...

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.

Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. Whi...

Advanced machine-learning techniques in drug discovery.

The popularity of machine learning (ML) across drug discovery continues to grow, yielding impressive results. As their use increases, so do their limi...

Predicting Post-Concussion Symptom Recovery in Adolescents Using a Novel Artificial Intelligence.

This pilot study explores the possibility of predicting post-concussion symptom recovery at one week post-injury using only objective diffusion tensor...

Post-DAE: Anatomically Plausible Segmentation via Post-Processing With Denoising Autoencoders.

We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical i...

Artificial neural network-based prediction of prolonged length of stay and need for post-acute care in acute coronary syndrome patients undergoing percutaneous coronary intervention.

BACKGROUND: Prolonged length of stay (LOS) and post-acute care after percutaneous coronary intervention (PCI) is common and costly. Risk models for pr...

Safety and effectiveness of ipragliflozin in Japanese patients with type 2 diabetes mellitus and impaired renal function: subgroup analysis of a 3-year post-marketing surveillance study (STELLA-LONG TERM).

STELLA-LONG TERM, a 3-year post-marketing surveillance study, evaluated the safety and effectiveness of the sodium-glucose cotransporter 2 inhibitor i...

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis.

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and a...