Fibromyalgia

OA-HybridCNN (OHC): An advanced deep learning fusion model for enhanced diagnostic accuracy in knee osteoarthritis imaging.

Knee osteoarthritis (KOA) is a leading cause of disability globally. Early and accurate diagnosis is paramount in preventing its progression and improving patients' quality of life. However, the inconsistency in radiologists' expertise and the onset of visual fatigue during prolonged image analysis often compromise diagnostic accuracy, highlighting the need for automated diagnostic solutions. In t...

Dynamic cross-domain transfer learning for driver fatigue monitoring: multi-modal sensor fusion with adaptive real-time personalizations.

Driver fatigue is one of the most common causes of road accidents, which means that there is a great need for robust and adaptive monitoring systems. Current models of fatigue detection suffer from domain-specific limitations in generalizing across diverse environments, sensor variability, and individual differences. Moreover, they are not resilient to real-time sensor quality issues or missing da...

Leveraging Artificial Intelligence for Digital Symptom Management in Oncology: The Development of CRCWeb.

UNSTRUCTURED: Digital health interventions offer promise for scalable and accessible healthcare, but access is still limited by some participatory cha...

Mismatch between warning information and protective behavior: Why experts + AI < 2?

Warning information plays a vital role in encouraging disaster preparedness among residents. Using survey experiment data from 619 respondents in Chin...

A dense multi-pooling convolutional network for driving fatigue detection.

Driver fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles, who are more susceptible to fatigue due to...

Performance Evaluation of Monocular Markerless Pose Estimation Systems for Industrial Exoskeletons.

Industrial exoskeletons (a.k.a. wearable robots) have been developed to reduce musculoskeletal fatigue and work injuries. Human joint kinematics and h...

Development of a Deep-Learning-Based Computerized Scoring Algorithm.

During polygraph tests, the examiner evaluates physiological responses recorded on a chart to identify deception. Generally, this evaluation involves ...

Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle.

The gait analysis has been applied in many fields, such as the assessment of falling, force evaluation in sports, and gait disorder detection for neur...

Machine Learning Models of Early Longitudinal Toxicity Trajectories Predict Cetuximab Concentration and Metastatic Colorectal Cancer Survival in the Canadian Cancer Trials Group/AGITG CO.17/20 Trials.

PURPOSE: Cetuximab (CET), targeting the epidermal growth factor receptor, is a systemic treatment option for patients with colorectal cancer. One know...

Optimized driver fatigue detection method using multimodal neural networks.

Driver fatigue is a significant factor contributing to road accidents, highlighting the need for precise and reliable detection systems. This study in...

DP-MP: a novel cross-subject fatigue detection framework with DANN-based prototypical representation and mix-up pairwise learning.

. Electroencephalography (EEG) is widely recognized as an effective method for detecting fatigue. However, practical applications of EEG for fatigue d...

Influence pathways of noise exposure on people's negative emotions and health across different activity contexts: A neural network-based double machine learning approach.

Noise is a major global environmental issue that raises concerns about both mental and physical health. However, few studies have investigated the med...

: Towards Autonomous Electronic Health Record Navigation.

Clinicians spend large amounts of time on clinical documentation, and inefficiencies impact quality of care and increase clinician burnout. Despite th...

Continuous real-time detection and management of comprehensive mental states using wireless soft multifunctional bioelectronics.

Quantitatively measuring human mental states that profoundly affect cognition, behavior, and recovery would revolutionize personalized digital healthc...

Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion.

Many traffic accidents occur nowadays as a result of drivers not paying enough attention or being vigilant. We call this driver sleepiness. This resul...

Neuronal and therapeutic perspectives on empathic pain: A rational insight.

Empathy is the capacity to experience and understand the feelings of others, thereby playing a key role in a person's mental well-being essentially by...

EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces.

Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brai...

Minimally invasive modalities for keratinocyte carcinomas-Part I: Diagnostics.

Keratinocyte carcinoma, including basal cell and squamous cell carcinoma, is the most prevalent form of cancer within the United States, with its inci...

Machine Learning Techniques for Simulating Human Psychophysical Testing of Low-Resolution Phosphene Face Images in Artificial Vision.

To evaluate the quality of artificial visual percepts generated by emerging methodologies, researchers often rely on labor-intensive and tedious human...

Few-shot transfer learning for individualized braking intent detection on neuromorphic hardware.

This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akid...