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Classification of knee osteoarthritis severity using markerless motion capture and long short-term memory fully convolutional network.

Computers in biology and medicine
This study explored the integration of markerless motion capture and deep learning to classify knee osteoarthritis severity based on gait kinematics, providing an alternative to traditional assessment methods. We employed a Long Short-Term Memory Ful...

Early detection of Alzheimer's disease using small RNAs. Results from the EPAD cohort.

The journal of prevention of Alzheimer's disease
BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia, and early diagnosis is crucial to enable effective interventions. Currently, Alzheimer's disease is diagnosed through cognitive assessments, brain imaging and fluid biomarkers ...

Predicting adolescents' environmental action: From individual to national-level factors using an explainable machine learning approach.

Journal of environmental management
As a key force in future environmental actions, youth play a crucial role in driving societal transformation. However, the factors influencing youth environmental actions have not been fully validated, and the role of national-level influences is oft...

The diagnostic model from semi-supervised cross modality transformation improved the distinguished ability of X-rays for pulmonary tuberculosis.

Clinical radiology
BACKGROUND: Early diagnosis of tuberculosis is particularly difficult in resource-poor areas. Traditional chest X-rays (CXR) have limited accuracy, while CT scans are costly and involve radiation exposure. The study aims to improve the diagnostic acc...

Explainable classification of Parkinson's disease with different motor subtypes by analyzing the synthetic MRI quantitative parameters of subcortical nuclei.

European journal of radiology
OBJECTIVES: To explore differences in quantitative parameters of subcortical nuclei using synthetic MRI across different motor subtypes of Parkinson's Disease (PD), and to develop an interpretable model for distinguishing PD subtypes.

Task-specific versus general-purpose AI models in ECG analysis: A comparative study with emergency medicine specialists.

The American journal of emergency medicine
PURPOSE: To evaluate and compare the diagnostic accuracy of three Artificial intelligence (AI) models-GPT-4o, Canva-GPT, and ECG Reader-GPT-against emergency medicine specialists (EMSs) in electrocardiogram (ECG) interpretation using a standardized a...

Regional cortical thinning and area reduction are associated with cognitive impairment in hemodialysis patients.

Brain research bulletin
Magnetic resonance imaging (MRI) has shown that patients with end-stage renal disease have decreased gray matter volume and density. However, the cortical area and thickness in patients on hemodialysis are uncertain, and the relationship between pati...

A comprehensive hybrid model: Combining bioinspired optimization and deep learning for Alzheimer's disease identification.

Computers in biology and medicine
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive ability and memory function. It is a progressive disease characterized by worsening dementia symptoms over time, starting with mild m...

Harnessing generative neural networks to fuse traditional Tujia Baishou dance with contemporary choreography: Enhancing creativity and aesthetic experience in dance students.

Acta psychologica
The objective of this study was to explore the potential of using the generative neural network Dance2Dance to integrate elements of the traditional Tujia Baishou dance with modern choreography, specifically examining the impact of such technologies ...

A Feasibility Study of a Video-Based Application by Parents of Infants Born Full-Term and Preterm.

Pediatric physical therapy : the official publication of the Section on Pediatrics of the American Physical Therapy Association
PURPOSE: To examine the factors that influence the usability of a video-based mobile application (app) by parents of infants born full-term and preterm.