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Recognizing drivers' sleep onset by detecting slow eye movement using a parallel multimodal one-dimensional convolutional neural network.
Comput Methods Biomech Biomed Engin
January 2025
School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, Changzhou University, Changzhou, P.R. China.
Slow eye movements (SEMs) are a reliable physiological marker of drivers' sleep onset, often accompanied by EEG alpha wave attenuation. A parallel multimodal 1D convolutional neural network (PM-1D-CNN) model is proposed to classify SEMs. The model uses two parallel 1D-CNN blocks to extract features from EOG and EEG signals, which are then fused and fed into fully connected layers for classification.
View Article and Find Full Text PDFCan Artificial Intelligence and Machine Learning Transform Prediction and Treatment of Post-Transjugular Intrahepatic Portosystemic Shunt (TIPS) Overt Hepatic Encephalopathy?
Gastro Hep Adv
September 2024
Blacktown Clinical School, School of Medicine, Western Sydney University, Penrith, New South Wales, Australia.
Integrating AI·IoT-OAHPs with existing elderly care systems.
Digit Health
January 2025
School of Public Affairs, Zhejiang University, Hangzhou, China.
This letter addresses the integration of artificial intelligence and the Internet of Things-based older adult healthcare programs with existing community and institutional elderly care systems. It highlights the current disconnect leading to service duplication and resource inefficiencies, proposes multifaceted integration approaches, and underscores the importance of supportive policies. International examples are referenced to demonstrate successful models, emphasizing the need for coordinated care to enhance service delivery and optimize resource use.
View Article and Find Full Text PDFA Deep Learning Framework for Automated Classification and Archiving of Orthodontic Diagnostic Documents.
Cureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFAI-guided virtual biopsy: Automated differentiation of cerebral gliomas from other benign and malignant MRI findings using deep learning.
Neurooncol Adv
January 2025
Institute for Artificial Intelligence in Medicine, University Hospital Essen, Germany.
Background: This study aimed to develop an automated algorithm to noninvasively distinguish gliomas from other intracranial pathologies, preventing misdiagnosis and ensuring accurate analysis before further glioma assessment.
Methods: A cohort of 1280 patients with a variety of intracranial pathologies was included. It comprised 218 gliomas (mean age 54.
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