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Revealing gait as a murine biomarker of injury, disease, and age with multivariate statistics and machine learning.

Scientific reports
Hundreds of rodent gait studies have been published over the past two decades, according to a PubMed search. Treadmill gait data, for example from the DigiGait system, generates over 30 + spatial and temporal measures. Despite this multi-dimensional ...

A deep learning algorithm for automatic 3D segmentation and quantification of hamstrings musculotendon injury from MRI.

Scientific reports
In high-velocity sports, hamstring strain injuries are common causes of missed play and have high rates of reinjury. Evaluating the severity and location of a hamstring strain injury, currently graded by a clinician using a semiqualitative muscle inj...

Wearable interactive full-body motion tracking and haptic feedback network systems with deep learning.

Nature communications
The increasing demand for motion tracking systems has been accelerated by advancements in virtual reality (VR) and motion reconstruction technologies. Combined with emerging innovations in the Internet of Things (IoT), these systems have unlocked tra...

Automated deep U-Net model for ischemic stroke lesion segmentation in the sub-acute phase.

Scientific reports
Manual segmentation of sub-acute ischemic stroke lesions in fluid-attenuated inversion recovery magnetic resonance imaging (FLAIR MRI) is time-consuming and subject to inter-observer variability, limiting clinical workflow efficiency. To develop and ...

Neurometabolic predictors of mental effort in the frontal cortex.

Translational psychiatry
Motivation drives individuals to overcome costs to achieve desired outcomes, such as rewards or avoidance of punishment, with significant variability across individuals. The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) ...

Predicting risk of early-onset sepsis in low-resource neonatal units using routine healthcare data: development and evaluation of multivariable statistical and machine learning models.

BMJ paediatrics open
BACKGROUND: Neonatal sepsis is a major cause of morbidity and mortality in low-resource settings and accurate, context-appropriate diagnostic methods are urgently needed to improve clinical outcomes.

Transformer-based AI approach to unravel long-term, time-dependent prognostic complexity in patients with advanced NSCLC and PD-L1 ≥50%: insights from the pembrolizumab 5-year global registry.

Journal for immunotherapy of cancer
BACKGROUND: With nearly one-third of patients with advanced non-small cell lung cancer (NSCLC) and PD-L1 Tumor Proportion Score≥50% surviving beyond 5 years following first-line pembrolizumab, long-term outcomes challenge traditional paradigms of can...

Preferences, Perceptions, and Use of Online Nutrition Content Among Young Australian Adults: Qualitative Study.

Journal of medical Internet research
BACKGROUND: Nutrition misinformation is pervasive on frequently accessed online sources such as social media platforms and websites. Young adults are at a high risk of viewing or engaging with this content due to their high internet and social media ...

Alzheimer's disease classification using a hybrid deep learning approach with multi-layer U-net segmentation and XAI driven analysis.

PloS one
Alzheimer's disease (AD) is a neurodegenerative illness causing a significant decrease in cognitive function, and early, accurate diagnosis is of great therapeutic and diagnostic value. Currently, there is promising potential for applying various typ...

Estimating heterogeneous impacts Of subsidised health insurance: A causal machine learning approach.

PloS one
The evaluation of social and health policies often necessitates understanding the variations in impacts based on recipients' observed characteristics, underscoring the value of estimating treatment effect heterogeneity. In this study, we leverage pre...