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Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.

NeuroImage
INTRODUCTION: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian comp...

A Cross-Sectional Survey of Optometrists in Canada Regarding Referral Patterns and a Needs Assessment for an Artificial Intelligence Referral Screening Tool for Epiretinal Membrane.

Ophthalmic surgery, lasers & imaging retina
BACKGROUND AND OBJECTIVE: This study evaluated optometrists' referral patterns for epiretinal membrane (ERM) patients in Ontario, Canada, and their attitudes towards an artificial intelligence (AI) tool for improving referral accuracy. An anonymous o...

Biomechanical Risk Classification in Repetitive Lifting Using Multi-Sensor Electromyography Data, Revised National Institute for Occupational Safety and Health Lifting Equation, and Deep Learning.

Biosensors
Repetitive lifting tasks in occupational settings often result in shoulder injuries, impacting both health and productivity. Accurately assessing the biomechanical risk of these tasks remains a significant challenge in occupational ergonomics, partic...

Predicting abatacept retention using machine learning.

Arthritis research & therapy
BACKGROUND: The incorporation of machine learning is becoming more prevalent in the clinical setting. By predicting clinical outcomes, machine learning can provide clinicians with a valuable tool for refining precision medicine approaches and improvi...

Me vs. the machine? Subjective evaluations of human- and AI-generated advice.

Scientific reports
Artificial intelligence ("AI") has the potential to vastly improve human decision-making. In line with this, researchers have increasingly sought to understand how people view AI, often documenting skepticism and even outright aversion to these tools...

Optimizing AI models to predict esophageal squamous cell carcinoma risk by incorporating small datasets of soft palate images.

Scientific reports
There is a currently an unmet need for non-invasive methods to predict the risk of esophageal squamous cell carcinoma (ESCC). Previously, we found that specific soft palate morphologies are strongly associated with increased ESCC risk. However, there...

Detection of sleep apnea using only inertial measurement unit signals from apple watch: a pilot-study with machine learning approach.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Despite increased awareness of sleep hygiene, over 80% of sleep apnea cases remain undiagnosed, underscoring the need for accessible screening methods. This study presents a method for detecting sleep apnea using data from the Apple Watch's ...

Artificial intelligence-based spatial analysis of tertiary lymphoid structures and clinical significance for endometrial cancer.

Cancer immunology, immunotherapy : CII
With the incorporation of immune checkpoint inhibitors into the treatment of endometrial cancer (EC), a deeper understanding of the tumor immune microenvironment is critical. Tertiary lymphoid structures (TLSs) are considered favorable prognostic fac...

Machine learning techniques for independent gait recovery prediction in acute anterior circulation ischemic stroke.

Journal of neuroengineering and rehabilitation
OBJECTIVE: This study aimed to develop and validate a machine learning-based predictive model for gait recovery in patients with acute anterior circulation ischemic stroke.