- Motor Imagery (MI) using Electroencephalography (EEG) is essential in Brain-Computer Interface (BCI) technology, enabling interaction with external devices by interpreting brain signals. Recent advancements in Convolutional Neural Networks (CNNs) h...
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...
Histopathological tissue grading is critical for disease diagnosis and treatment, but manual grading is labor-intensive and time-consuming, requiring expert pathologists. This study presents an efficient analysis of squamous cell carcinoma (SCC) hist...
Electrocardiogram (ECG) signals play a critical role in diagnosing cardiovascular diseases (CVDs), yet automated ECG classification remains challenging due to inter-patient variability, signal noise, and heart rhythm complexity. To address these chal...
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 ...
With the rapid advancement of artificial intelligence technology, efficiently extracting and analyzing music performance style features has become an important topic in the field of music information processing. This work focuses on the classificatio...
OBJECTIVES: The objective of this study is to evaluate whether large language models (LLMs) can achieve performance comparable to expert-developed deep neural networks in detecting flow starvation (FS) asynchronies during mechanical ventilation.
UNLABELLED: Early detection of glaucoma represents a vital factor in securing vision while the disease retains its position as one of the central causes of blindness worldwide. The current glaucoma screening strategies with expert interpretation depe...
Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studi...
Journal of neuroradiology = Journal de neuroradiologie
Jun 26, 2025
BACKGROUND: The Thrombolysis in Cerebral Infarction (TICI) scale is widely used to assess angiographic outcomes of mechanical thrombectomy despite significant variability. Our objective was to create and optimize an artificial intelligence (AI)-based...
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