Pain Management

GAT-BiGRU: explainable multi-task temporal graph learning for glucose forecasting, hypoglycemia risk, and counterfactual insulin adjustment.

BACKGROUND: People with type 1 diabetes mellitus (T1DM) show glucose variability driven by insulin dosing, meals, activity, and circadian rhythms. Many deep learning approaches treat glucose forecasting and hypoglycemia detection as separate tasks and provide limited transparency. OBJECTIVE: We developed an explainable, multi-task temporal graph framework that jointly predicts glucose trajectories...

From Intradiscal Pressure to Multimodal Estimation of Lumbar Spinal Loads.

BACKGROUND: Estimation of lumbar spinal loads is important for understanding low back pain, guiding ergonomic interventions, and informing surgical and rehabilitation planning. Historically, intradiscal pressure (IDP) provided one of the few internal in vivo measures of disc loading; more recently, telemetry, musculoskeletal (MS) modeling, finite element (FE) analysis, hybrid MS-FE approaches, dis...

Machine learning-based predictive model for sleep disorders in diabetic patients: data analysis from CHARLS.

Sleep disorders are prevalent and constitute a major concern in patients with diabetes mellitus. Therefore, the aim of this study was to investigate t...

Factors Associated With an LLM Arriving at the Same Diagnosis as a Musculoskeletal Specialist.

INTRODUCTION: Large language models (LLMs) can generate plausible diagnoses from patient symptom descriptions and convey complex medical information i...

Integrating machine learning and pathway modelling to explore factors associated with chronic post-surgical pain and quality of life: a secondary observational analysis of the ENIGMA-II trial.

BACKGROUND: Chronic post-surgical pain (CPSP) is a common long-term complication with multifactorial contributors, and improved risk stratification re...

Transformer-based models in dentistry: a systematic review.

BACKGROUND: Transformer-based architectures have rapidly gained prominence in medical imaging due to their ability to model long-range dependencies an...

Psychometric Evaluation and Discriminative Validity of the Short-Form Brief Pain Inventory Across Nociceptive, Neuropathic, and Nociplastic Pain Mechanisms.

The measurement performance of the Short-Form Brief Pain Inventory (BPI-SF) across IASP-recognized pain mechanisms remains unclear. We evaluated the r...

Deep Learning Assisted Double Inversion Recovery improves Focal Lesion Detection in Optic Neuropathy.

OBJECTIVES: To compare the acquisition time, image quality, and diagnostic confidence of DL-accelerated DIR (DIR-DL) with conventional MRI in patients...

Confidence-aware semi-supervised siamese graph networks for semantic text similarity in low-resource Urdu.

Accurate detection of semantically similar texts underpins applications such as plagiarism detection and content recommendation, yet remains challengi...

Machine Diagnostics and Machine Phenotyping of Migraine: A HUNT Study.

BACKGROUND AND OBJECTIVES: In the absence of biomarkers, the true biological footprint of migraine remains incompletely understood. It could perhaps b...

Is Migraine a Single Entity?: Machine Learning Reveals Underlying Heterogeneity.

A Novel Machine Learning Signature Incorporating Lipid and Inflammatory Biomarkers to Predict Pulsed Radiofrequency Efficacy in Zoster-Associated Pain.

INTRODUCTION: Pulsed radiofrequency (PRF) is a pivotal neuromodulation strategy for zoster-associated pain (ZAP); however, clinical outcomes exhibit s...

Multimodal Information-Driven Heterogeneous Graph Neural Networks for Protein-Ligand Binding Affinity Prediction.

Accurate prediction of protein-ligand binding affinity (PLA) is essential for efficient drug screening. However, existing methods often inadequately m...

Severity-dependent alterations of functional network segregation and integration in tobacco use disorder.

BACKGROUND: Previous research on brain network topology in Tobacco Use Disorder (TUD) has been inconsistent, likely due to overlooking the heterogenei...

Towards clinically interpretable machine learning in emergency surgery: feature importance and insights across clinical time points in abdominal pain cases.

PURPOSE: Machine learning (ML) may support decision-making for acute abdominal pain (AAP), but limited interpretability hinders adoption. We evaluated...

Generative AI dependency on programming among university students: a scale development and validation study.

With the rapid integration of generative artificial intelligence into programming education, concerns have emerged regarding university students' pote...

Towards a rational ECT treatment approach for severe and treatment resistant schizophrenia using neuroimaging biomarkers: A focused review.

Treatment resistant schizophrenia (TRS) is a major challenge in psychiatry, and its management remains an unmet need. Given the relatively high preval...

Joint-specific measures improve risk adjustment in total knee arthroplasty: A machine learning approach.

PURPOSE: Accurate risk adjustment in total knee arthroplasty (TKA) is essential for outcome prediction and quality assessment. Most existing predictio...

AI-planned and robotic-assisted pedicle screw placement improves accuracy and reduces loosening in lumbar fusion for patients with low bone mass: a multicenter study.

OBJECTIVE: This study aimed to evaluate the effectiveness of artificial intelligence (AI)-based pedicle screw trajectory planning combined with roboti...

An Interpretable Deep Learning Framework Leveraging RNA Foundation Model and Capsule Networks for Accurate Prediction of RNA 2'-O-Methylation Sites.

RNA 2'-O-methylation (2OMe) is a widespread post-transcriptional modification that influences RNA stability, translation, and immune recognition. Yet,...