Journal of neuroengineering and rehabilitation
Oct 9, 2025
BACKGROUND: Elderly patients often face challenges in recovering from distal radius fractures (DRFs), and inadequately guided rehabilitation may lead to delayed healing or secondary injury.
BACKGROUND: To develop a new deep learning model for detecting white spot lesions (WSLs), which are commonly observed in patients undergoing orthodontic treatment, and assess its accuracy.
BACKGROUND: This study aimed to evaluate the diagnostic accuracy of artificial intelligence (AI) models in predicting dental extractions during orthodontic treatment planning.
BACKGROUND: Artificial intelligence (AI) is rapidly transforming healthcare, including dentistry, through its applications in diagnosis, prosthetic planning, and oral disease detection. As future professionals, dental students play a vital role in in...
BACKGROUND: Previous studies have demonstrated that the triglyceride-glucose (TyG) index in combination with the estimated glucose disposal rate (eGDR) could predict mortality risks in the normal population. Our studies have focused on their additive...
To improve the computational efficiency of olfactory neural network, this paper proposes a multithreading-based parallel computing method. Firstly, focusing on the olfactory neural network and its neuronal equations, this paper analyzes and compares ...
Healthcare providers (HCPs) in the intensive care unit (ICU) frequently face information overload, which can result in cognitive fatigue and decision-making errors. This study compares the efficiency and accuracy of data collection between an artific...
PURPOSE OF REVIEW: This review explores the role of artificial intelligence (AI) in visceral adipose tissue (VAT) and ectopic fat imaging. It aims to evaluate how AI may be used to enhance the efficiency and accuracy of cardiovascular disease (CVD) r...
Mild cognitive impairment (MCI) is a prodromal stage of dementia, and its early detection is critical for improving clinical outcomes. However, current diagnostic tools such as brain magnetic resonance imaging (MRI) and neuropsychological testing hav...
Data sparseness is a major limiting factor for deep machine learning. In the natural sciences, data distributions are heterogeneous. For instance, in chemistry and early-phase drug discovery, compound and molecular property data are typically sparse ...
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