Pain Management

Applying machine learning in predicting medication treatment outcomes for opioid use disorder.

BACKGROUND: While medication for opioid use disorder (MOUD) is effective for a significant proportion of patients, many return to using opioids during treatment. Understanding which factors lead to successful treatment informs the development of implementation approaches that can improve outcomes. This manuscript and its accompanying website provide an applied introduction to interpretable machine...

Pharmacoepidemiologic characterization of cannabis use and symptomatology in rheumatology using natural language processing of electronic health record clinic notes.

Up to 70% of patients with autoimmune rheumatic diseases (ARDs), including rheumatoid arthritis, psoriatic arthritis, and systemic lupus erythematosus, report moderate to severe pain despite controlled inflammation, driving interest in self-management including use of cannabis. We applied natural language processing (NLP) to 2.6 million electronic health record notes from 5051 adults with ARDs see...

Disability pension during emerging adulthood: Insights from the young-HUNT study on psychological distress, chronic pain, and policy reform.

INTRODUCTION: Disability pension (DP) among young adults has steadily increased in Norway, with chronic pain and psychological distress among the main...

Deep learning-based classification of acute scrotum using single ultrasound images.

OBJECTIVE: To develop a deep learning model for differential diagnosis of acute scrotum using single ultrasound (US) images. PATIENTS AND METHODS: We ...

The appropriateness of primary care physician referrals for lumbar spine magnetic resonance imaging (MRI): retrospective cross-sectional study.

AIM: Low back pain (LBP) affects a significant proportion of the population. There has been an increase in referrals for magnetic resonance imaging (M...

Applications of artificial intelligence-based conversational agents in healthcare: A systematic umbrella review.

BACKGROUND: Artificial intelligence-based conversational agents (AI-based CAs) have emerged as essential tools for communication and service delivery ...

Preliminary findings on a deep learning model using electroencephalogram for multi-level neuropathic pain detection in post-stroke patients.

AIM: Neuropathic pain occurs commonly after stroke and represents a major source of disability for affected patients. This study aims to develop an ac...

MC-HSTA: A multi-source cross-domain hybrid spatio-temporal attention network for traffic flow prediction.

Conventional representation learning methods have achieved remarkable performance in traffic flow forecasting when data is sufficient, while they stru...

Imaging of vestibular function and disorders and its clinical relevance.

PURPOSE OF REVIEW: To discuss recent advances in imaging of the structural organization and functional connectivity of central vestibular disorders wi...

Predicting Pharmacological Treatment Response in Migraine Using AI/ML: A Scoping Review of the Evidence and Future Directions.

The treatment of migraine is hampered by inter-individual variability, leading to an inefficient "trial and error" approach. Artificial intelligence (...

Unsupervised SAM-guided mixture-of-multimodal-experts fusion network for medical image diagnosis.

Accurate diagnosis of cancer from medical images relies on both precise lesion localization and complementary multimodal information. However, current...

Predicting Surgical Outcomes in Chronic Rhinosinusitis From Preoperative Patient Data: A Machine Learning Approach.

BACKGROUND: Endoscopic sinus surgery (ESS) fails to adequately address symptoms in some chronic rhinosinusitis (CRS) patients. This study aims to eval...

Artificial intelligence in spine care: A scoping review of treatment applications.

BACKGROUND: Artificial intelligence (AI) is increasingly applied in healthcare to support decision-making, personalize treatment, and improve outcomes...

SVE-Former: A fast fourier transformer via singular vector embedding.

Self-attention is the cornerstone of transformers, yet its quadratic time and space complexity with respect to the input sequence length leads to high...

HPCSMN: A Classification Method of Chemotherapy Sensitivity of Hypopharyngeal Cancer Based on Multimodal Network.

The treatment of hypopharyngeal cancer faces complex challenges, and accurate prediction of chemotherapy sensitivity is crucial for personalized treat...

An Interpretable Machine Learning Model Based on MRI Features for Predicting Pain Severity in Temporomandibular Disorders.

BACKGROUND: Chronic pain around the temporomandibular joint (TMJ) and masticatory muscles is a primary symptom of temporomandibular disorders (TMD). H...

Ethics, economics, and patient-centered outcomes of spinal cord stimulation.

PURPOSE OF THE REVIEW: With the widespread integration of spinal cord stimulation (SCS) into clinical practice, understanding its ethical, economic, a...

DoubleBlock-ViT: A MaxViT-based enhancement and dual-path skip connections for brain tumor segmentation in MRI scans.

BACKGROUND: Manual interpretation of brain tumor regions in MRI scans demands substantial medical expertise, is time-consuming, and is prone to human ...

Mental health at risk: Predicting psychological distress in Australian youth through machine learning models.

BACKGROUND: Psychological distress among youth is a growing global health concern and a leading non-communicable disease burden. Early and accurate pr...

Design and optimization of indomethacin-loaded solid dispersion orally dissolving films: Machine learning and molecular dynamics study.

Indomethacin (IND) is a nonsteroidal anti-inflammatory drug (NSAID) with anti-inflammatory, analgesic, and antipyretic properties, and is widely used ...