Pain Management

Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing.

Chronic pain is prevalent and disproportionately impacts adults with a lower quality of life. Although subjective self-reporting is the "gold standard" for pain assessment, tools are needed to objectively monitor and account for inter-individual differences. This study introduced a novel framework to objectively classify pain intensity levels using physiological signals during Quantitative Sensory...

Machine-learning-based prediction of functional recovery in deep-pain-negative dogs after decompressive thoracolumbar hemilaminectomy for acute intervertebral disc extrusion.

OBJECTIVE: To develop and compare machine-learning algorithms to predict recovery of ambulation after decompressive surgery for acute intervertebral disc extrusion (IVDE).

Deep reinforced cognitive analytics algorithm (DRCAM): An advanced method to early detection of cognitive skill impairment using deep learning and reinforcement learning.

A Deep Reinforced Cognitive Analytics Model (DRCAM) has been proposed in this work, integrating multimodal learning and reinforcement-based interventi...

Integrating data mining with transcranial focused ultrasound to refine neuralgia treatment strategies.

BACKGROUND: Neuralgia and other neuropathic pain are difficult to treat owing to their complicated etiology and a wide variety of responses to treatme...

SpineMamba: Enhancing 3D spinal segmentation in clinical imaging through residual visual Mamba layers and shape priors.

Accurate segmentation of three-dimensional (3D) clinical medical images is critical for the diagnosis and treatment of spinal diseases. However, the c...

A longitudinal observational study with ecological momentary assessment and deep learning to predict non-prescribed opioid use, treatment retention, and medication nonadherence among persons receiving medication treatment for opioid use disorder.

BACKGROUND: Despite effective treatments for opioid use disorder (OUD), relapse and treatment drop-out diminish their efficacy, increasing the risks o...

Machine Learning of Smoking Relapse: the Role of Racial Differences and E-Cigarette Vaping Characteristics on Former Smokers.

BACKGROUND: Machine learning models can help identify multifaceted factors influencing tobacco use transitions. A random forest model is developed to ...

Machine learning-based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes, marked by symptoms like hyperalgesia, numbness, and swe...

Unveiling CNS cell morphology with deep learning: A gateway to anti-inflammatory compound screening.

Deciphering the complex relationships between cellular morphology and phenotypic manifestations is crucial for understanding cell behavior, particular...

Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data.

Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress...

Transformative Effects of Mindfulness Meditation Training on the Dynamic Reconfiguration of Executive and Default Mode Networks in Internet Gaming Disorder.

BACKGROUND: Internet gaming disorder (IGD) is a pervasive global mental health issue, and finding effective treatments for the disorder has been chall...

Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques.

Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting ...

Battery state of health estimation under fast charging via deep transfer learning.

Accurate state of health (SOH) estimation is essential for effective lithium-ion battery management, particularly under fast-charging conditions with ...

Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain.

Spinal cord stimulation (SCS) is a well-accepted therapy for refractory chronic pain. However, predicting responders remain a challenge due to a lack ...

A hybrid vine copula-fuzzy model for groundwater level simulation under uncertainty.

Accurate simulation of groundwater level is crucial for the sustainable management of water resources. However, the numerous uncertainties in input da...

The need for epistemic humility in AI-assisted pain assessment.

It has been difficult historically for physicians, patients, and philosophers alike to quantify pain given that pain is commonly understood as an indi...

Machine learning models and classification algorithms in the diagnosis of vestibular migraine: A systematic review and meta-analysis.

OBJECTIVES: To perform a systematic review and meta-analysis to evaluate the effectiveness of machine learning (ML) algorithms in the diagnosis of ves...

Neuronal and therapeutic perspectives on empathic pain: A rational insight.

Empathy is the capacity to experience and understand the feelings of others, thereby playing a key role in a person's mental well-being essentially by...

Pixel level deep reinforcement learning for accurate and robust medical image segmentation.

Existing deep learning methods have achieved significant success in medical image segmentation. However, this success largely relies on stacking advan...

FEGGNN: Feature-Enhanced Gated Graph Neural Network for robust few-shot skin disease classification.

Accurate and timely classification of skin diseases is essential for effective dermatological diagnosis. However, the limited availability of annotate...