Pain Management

Effectiveness of tele-rehabilitation using AI-guided exercise and pain neuroscience education for fibromyalgia (FIBROIA): Protocol for a randomized controlled trial.

INTRODUCTION: Fibromyalgia (FM) is a chronic condition characterized by widespread pain and cognitive dysfunction, with pharmacological treatments offering limited efficacy. Although Pain Neuroscience Education (PNE) and therapeutic exercise are evidence-based interventions, accessibility and adherence remain major challenges particularly in underserved regions such as Latin America. This trial in...

Distinguishing Pain and No Pain in Musicians Through Machine Learning Analysis of Musculoskeletal Data.

Musculoskeletal disorders are common among professional musicians and often linked to altered movement patterns. This study examined whether a combined Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) framework can identify interpretable motion features associated with shoulder-neck pain during violin performance. Kinematic data from 26 violinists (11 with pain, 15 pain-free) we...

Supervised Learning Provides Small but Consistent Improvements to Clustering when Predicting Chronic Pain Outcomes Following Treatment.

Using registry and questionnaire data from 47,235 chronic pain patients, we evaluated whether supervised learning outperforms clustering in predicting...

Headache Diagnosis with Open Language Models on German Vignettes: Study Protocol.

Headache disorders present diagnostic challenges due to their clinical heterogeneity and the extensive taxonomy of the ICHD-3 classification. While la...

Precision pharmacology of CNS GPCRs: From biased signaling to translational therapeutics.

G protein-coupled receptors (GPCRs) represent one of the most extensively targeted receptor families in central nervous system (CNS) pharmacology. Rec...

CDM-UNet: Content-Driven Enhanced Mamba Model for Medical Image Segmentation.

Segmenting medical images plays a pivotal role in diagnosis, treatment planning, and healthcare. Recent advancements in deep learning have significant...

Impact of COVID-19-related data drift on machine-learning prognostic models predicting 30-day opioid-related emergency department visits, hospitalisation or mortality: a population-level administrative data study in Alberta, Canada.

OBJECTIVE: To develop machine-learning (ML) models during the COVID-19 pandemic and adjacent time periods to evaluate the impact of data drift on mode...

Altered EEG markers of reward learning during abstinence in alcohol dependence: A probabilistic reversal learning study.

OBJECTIVE: This study investigated neurophysiological and behavioural adaptations in reward learning and decision making which may contribute to the d...

An explainable machine learning framework for analyzing and predicting mental health problems among university students in Bangladesh.

Mental health problems (MHPs) among university students are an increasing public health concern globally, including in Bangladesh. While machine learn...

Development, Feasibility, Acceptability, and Usability of an Artificial Intelligence-Powered Chatbot (Suzy) to Support Patients in Substance Use Disorder Recovery: Multiphase Study.

BACKGROUND: Substance use disorder (SUD) remains a major public health crisis in the United States, with significant challenges in treatment access, r...

Artificial intelligence and neurodegeneration: state of the art and explainability.

BACKGROUND: Alzheimer disease (AD) and Parkinson disease (PD) are an increasing healthcare concern and growing cause of disability in our century. The...

AI-Driven Prediction of Cancer Pain Episodes: A Hybrid Decision Support Approach.

Lung cancer patients frequently experience breakthrough pain episodes, with up to 91% requiring timely intervention. To enable proactive pain manageme...

From EEG signals to quantitative assessment: predicting depression severity using a novel deep learning framework.

The assessment of depression severity still relies primarily on subjective rating scales, with a lack of objective quantitative biomarkers. This study...

Robust transcriptomic signatures of Alzheimer's disease progression: validated explainable AI approach.

The lack of validated stage-specific biomarkers hampers the understanding of Alzheimer's disease (AD) progression and clinical translation. Current tr...

Deep Learning outperforms physicians in myopathy and neuropathy classification based on Needle Electromyography Signal.

Needle electromyography (nEMG) is a valuable tool for diagnosing patients with neuromuscular diseases. However, it is labor-intensive and is prone to ...

The state of motor neural networks in low back pain a systematic review and meta-analysis.

Low back pain (LBP) is one of the most prevalent musculoskeletal disorders and often shows poor recovery. Although symptom persistence is multifactori...

Connected parents, connected children: the relationship of smartphone, social media, and AI with parenting and children's tech consumption in Türkiye.

BACKGROUND: The objective of this study is to investigate parental utilization of smartphones, engagement with social media, and adoption of artificia...

Chatbot relational dependence and psychosis risk: Conversational artificial intelligence as a potential behavioral addiction context.

Conversational artificial intelligence (AI) chatbots are increasingly used for emotional support, companionship, and psychological reflection. Their c...

Evaluating Medical Students' Perceptions of AI-Assisted Clinical Documentation (CarePilot): Cross-Sectional Study.

BACKGROUND: Artificial intelligence (AI) is increasingly being integrated into health care to streamline documentation and improve clinician efficienc...

PGCFuse: a U-Net-based progressive gated convmixer network with global context aggregation for infrared-visible image fusion.

Infrared (IR) and visible (VI) image fusion aims to integrate complementary information from heterogeneous modalities to enhance visual perception in ...