Pain Management

Using artificial intelligence to predict sexual health outcomes in endometriosis: a decision tree model algorithm.

BACKGROUND: Endometriosis profoundly impairs sexual function through complex interactions between pain, hormonal disturbances, psychological distress, and sociodemographic factors. AIM: To develop and validate a decision tree-based model identifying key predictors of sexual dysfunction in women with endometriosis. METHODS: We conducted a cross-sectional online survey among 1586 women with endometr...

Concussion pathophysiology: From biomechanical insult to clinical phenotype - or is injury truly the beginning?

Mild traumatic brain injury (mTBI) is associated with substantial morbidity worldwide. Emerging evidence demonstrates that both impact- and blast-related mTBI produce diffuse microstructural and functional alterations, e.g., diffuse axonal injury, astroglial and microglial activation, cerebrovascular dysfunction, and neurometabolic disturbance. Rotational acceleration in impact injury preferential...

Development and validation of a risk prediction model for diabetic peripheral neuropathic pain in type 2 diabetes: A machine learning and statistical approach.

OBJECTIVE: To identify independent risk factors for diabetic peripheral neuropathic pain (DPNP), construct a nomogram prediction model, and quantify t...

DMSTG-AD: an SDN intrusion detection method based on dynamic multi-scale spatio-temporal graph neural network.

Software-defined networking (SDN) centralizes control, simplifying network management and configuration while simultaneously increasing vulnerability ...

Teriparatide reduces spinal neuroinflammatory phenotypes in ovariectomized rats revealed by an artificial intelligence-driven glial cell morphometry of bright-field tissue sections.

Osteoporosis frequently presents with lower back pain, often accompanied by hypersensitivity, even in the absence of vertebral fractures, suggesting t...

Application of Artificial Intelligence in MedDRA Coding: A Practical Exploration from Clinical Data Management Perspective.

BACKGROUND: Traditional manual MedDRA coding in clinical data management (CDM) faces persistent challenges, including suboptimal site data quality, te...

Evaluating the accuracy of screening titles and abstracts for progressing to full-text screening by one vs two independent reviewers: part 1 of a meta-research study of rapid review methodological trade-offs.

OBJECTIVES: To determine the accuracy for progressing records to full-text screening using one vs two reviewers to screen title and abstracts in 3 rev...

Application of Artificial Intelligence in Chronic Pain: Bibliometric Analysis.

BACKGROUND: In recent years, artificial intelligence (AI) has demonstrated great potential in the field of managing chronic pain (CP). AI can optimize...

A Therapeutic Conversational Agent (Solace) for Management of Chronic Pain: Acceptability and Usability Study.

BACKGROUND: Chronic pain is a critical cause of personal suffering and societal concern. However, treatment options remain inadequate, and access to e...

Chronic phase of Chikungunya: understanding the impact of joint pain using data science and artificial intelligence.

BACKGROUND: Chikungunya is an arbovirus capable of affecting the musculoskeletal system of infected individuals. Furthermore, it has the potential to ...

Stereotactic Body Radiotherapy for Spinal Metastases: Outcomes and Development of a Risk Assessment Score to Predict Local Tumor Progression.

BACKGROUND AND OBJECTIVES: Stereotactic body radiotherapy (SBRT) has emerged as an effective treatment modality for spinal metastases. However, high-p...

Exploration of a Multimodal Machine Learning Model Integrating Ultrasound and Clinical Indicators for the Diagnosis of Diabetic Peripheral Neuropathy.

OBJECTIVES: Based on ultrasound technology and clinical indicators, this study intends to develop multiple risk prediction models for diabetic periphe...

Population-Level Digital Stroke Surveillance: Building a Fair and Accurate ICD-10 Detection Model.

Background The International Classification of Diseases, 10th Revision (ICD-10), is widely used for clinical care, quality assurance, and stroke resea...

Profiling of hypoxic and extracellular matrix remodeling markers in biofluids and cerebral tissue after traumatic brain injury: expression and signaling crosstalk of HIF-1α, MMP-9/14 and EPHA2.

Traumatic brain injury (TBI) remains a formidable clinical, neuropathological, and forensic challenge, constituting a leading cause of death as well a...

MSCMH-Net: A multi-scale channel-mixing hybrid network for whole-brain segmentation.

Whole-brain segmentation constitutes a fundamental task in medical image analysis, providing quantitative assessment of fine-grained brain regions and...

Predicting self-image satisfaction after adult spinal deformity surgery: a machine learning approach using patient phenotypes.

PURPOSE: To characterize the evolution of the Self-Image (SI) domain of the Scoliosis Research Society-22 item questionnaire (SRS-22) in adult spinal ...

Utilization and Feasibility of a Wearable Device in Patients With Sedative Effects of Drugs: Protocol for a Prospective Observational Study for the Advanced Respiratory Monitoring Events in Drug Toxicity (ARM-ED) Study.

BACKGROUND: Drug-related deaths worldwide are most commonly attributed to opioids. Opioids and other sedative drugs can cause respiratory depression a...

Comparing the performance of deep learning video-based models and trained veterinarians in cattle pain assessment.

Accurate pain assessment in animals is crucial for ensuring animal welfare and guiding veterinary interventions. Traditional pain evaluation relies on...

An AI-based nomogram (the CGRP score) for the prediction of response to modern migraine therapies: an independent assessment and some considerations on the application of AI tools in clinical setting.

Keros-Net: a convolutional block attention module-squeeze-and-excitation-integrated hybrid learning framework for olfactory fossa depth classification.

PURPOSE: This study aimed to develop a hybrid decision support framework combining deep learning (DL) and machine learning (ML) to automatically class...