AIMC Topic: Pain Measurement

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A stacking ensemble machine learning model for predicting postoperative axial pain intensity in patients with degenerative cervical myelopathy.

Scientific reports
Machine learning (ML) has been extensively utilized to predict complications associated with various diseases. This study aimed to develop ML-based classifiers employing a stacking ensemble strategy to forecast the intensity of postoperative axial pa...

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

Scientific reports
Spinal cord stimulation (SCS) is a well-accepted therapy for refractory chronic pain. However, predicting responders remain a challenge due to a lack of objective pain biomarkers. The present study applies machine learning to predict which patients w...

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

Medicine, health care, and philosophy
It has been difficult historically for physicians, patients, and philosophers alike to quantify pain given that pain is commonly understood as an individual and subjective experience. The process of measuring and diagnosing pain is often a fraught an...

Outcomes of lateral femoral cutaneous nerve decompression surgery in meralgia paraesthetica: assessment of pain, sensory deficits, and quality of life.

International orthopaedics
PURPOSE: Meralgia paraesthetica (MP) is a rare neuropathy of the lateral femoral cutaneous nerve (LFCN), characterized by thigh pain, paraesthesia, or sensory loss. When conservative treatments fail, surgical interventions such as neurolysis or neure...

Comparison of predictive models for knee pain and analysis of individual and physical activity variables using interpretable machine learning.

The Knee
BACKGROUND: Knee pain is associated with not only individual factors such as age and obesity but also physical activity factors such as occupational activities and exercise, which has a significant impact on the lives of adults and the elderly.

Disclosing neonatal pain in real-time: AI-derived pain sign from continuous assessment of facial expressions.

Computers in biology and medicine
This study introduces an AI-derived pain sign for continuous neonatal pain assessment, addressing the limitations of existing pain scales and computational approaches. Traditional pain scales, though widely used, are hindered by inter-rater variabili...

Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique.

Scientific reports
Surgical castration of males is carried out on a large scale in the US swine industry and the pain resulting from this procedure can be assessed using the Unesp-Botucatu pig composite acute pain scale (UPAPS). We aim to propose a short version of UPA...

A Novel Framework for Quantum-Enhanced Federated Learning with Edge Computing for Advanced Pain Assessment Using ECG Signals via Continuous Wavelet Transform Images.

Sensors (Basel, Switzerland)
Our research introduces a framework that integrates edge computing, quantum transfer learning, and federated learning to revolutionize pain level assessment through ECG signal analysis. The primary focus lies in developing a robust, privacy-preservin...

Exploring the Effect of an 8-Week AI-Composed Exercise Program on Pain Intensity and Well-Being in Patients With Spinal Pain: Retrospective Cohort Analysis.

JMIR formative research
BACKGROUND: Spinal pain, one of the most common musculoskeletal disorders (MSDs), significantly impacts the quality of life due to chronic pain and disability. Physical activity has shown promise in managing spinal pain, although optimizing adherence...

A Multimodal Pain Sentiment Analysis System Using Ensembled Deep Learning Approaches for IoT-Enabled Healthcare Framework.

Sensors (Basel, Switzerland)
This study introduces a multimodal sentiment analysis system to assess and recognize human pain sentiments within an Internet of Things (IoT)-enabled healthcare framework. This system integrates facial expressions and speech-audio recordings to evalu...