Fibromyalgia

A Comparison of Three Automated Root-Knot Nematode Egg Counting Approaches Using Machine Learning, Image Analysis, and a Hybrid Model.

spp. (root-knot nematodes [RKNs]) are a major threat to a wide range of agricultural crops worldwide. Breeding crops for RKN resistance is an effective management strategy, yet assaying large numbers of breeding lines requires laborious bioassays that are time-consuming and require experienced researchers. In these bioassays, quantifying nematode eggs through manual counting is considered the cur...

Investigation of the potential molecular mechanisms of acupuncture in the treatment of long COVID: a bioinformatics approach.

Long COVID is a poorly understood condition characterized by persistent symptoms following the acute phase of COVID-19, including fatigue, cognitive impairment, and joint pain. Acupuncture, a key component of traditional Chinese medicine treatment, has shown potential in alleviating long COVID symptoms. However, the molecular mechanisms underlying its therapeutic effects remain largely unknown. In...

Enhancing Upper Limb Function and Motor Skills Post-Stroke Through an Upper Limb Rehabilitation Robot.

Cerebrovascular accidents, commonly known as strokes, represent a prevalent neurological event leading to significant upper limb disabilities, thereby...

Research on fatigue detection of flight trainees based on face EMF feature model combination with PSO-CNN algorithm.

Even though the capability of aircraft manufacturing has improved, human factors still play a pivotal role in flight accidents. For example, fatigue-r...

A new modular neuroprosthesis suitable for hybrid FES-robot applications and tailored assistance.

BACKGROUND: To overcome the application limitations of functional electrical stimulation (FES), such as fatigue or nonlinear muscle response, the comb...

Research on low-power driving fatigue monitoring method based on spiking neural network.

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance f...

Decoding IBS: a machine learning approach to psychological distress and gut-brain interaction.

PURPOSE: Irritable bowel syndrome (IBS) is a diagnosis defined by gastrointestinal (GI) symptoms like abdominal pain and changes associated with defec...

User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface.

This study evaluates an innovative control approach to assistive robotics by integrating brain-computer interface (BCI) technology and eye tracking in...

Improving accuracy and efficiency of the machined PEEK denture based on NSGA-II integrated GABP neural network.

OBJECTIVES: The polymer polyetheretherketone (PEEK) is gradually being used in dental restorations because of its excellent mechanical properties, che...

Deep learning enables accurate soft tissue tendon deformation estimation in vivo via ultrasound imaging.

Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue ...

Single-center outcomes of artificial intelligence in management of pulmonary embolism and pulmonary embolism response team activation.

Multidisciplinary pulmonary embolism response teams (PERTs) have shown that timely triage expedites treatment. The use of artificial intelligence (AI)...

Additional Rehabilitative Robot-Assisted Gait Training for Ambulation in Geriatric Individuals with Guillain-Barré Syndrome: A Case Report.

We present a case of a 75-year-old Asian woman with Guillain-Barré syndrome (GBS) who underwent a 1-month comprehensive rehabilitation training progra...

MEFFNet: Forecasting Myoelectric Indices of Muscle Fatigue in Healthy and Post-Stroke During Voluntary and FES-Induced Dynamic Contractions.

Myoelectric indices forecasting is important for muscle fatigue monitoring in wearable technologies, adaptive control of assistive devices like exoske...

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods.

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mor...

Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities.

The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical hea...

LGGNet: Learning From Local-Global-Graph Representations for Brain-Computer Interface.

Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn ...

The effect of robot-assisted versus standard training on motor function following subacute rehabilitation after ischemic stroke - protocol for a randomised controlled trial nested in a prospective cohort (RoboRehab).

BACKGROUND: Body weight unloaded treadmill training has shown limited efficacy in further improving functional capacity after subacute rehabilitation ...

Factors influencing psychological distress among breast cancer survivors using machine learning techniques.

Breast cancer is the most commonly diagnosed cancer among women worldwide. Breast cancer patients experience significant distress relating to their di...

Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detect...

Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model.

BACKGROUND AND OBJECTIVE: Cardiovascular disease (CD) is a major global health concern, affecting millions with symptoms like fatigue and chest discom...