AIMC Topic: Low Back Pain

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GLEAM: A multimodal deep learning framework for chronic lower back pain detection using EEG and sEMG signals.

Computers in biology and medicine
Low Back Pain (LBP) is the most prevalent musculoskeletal condition worldwide and a leading cause of disability, significantly affecting mobility, work productivity, and overall quality of life. Due to its high prevalence and substantial economic bur...

Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review.

BMC musculoskeletal disorders
BACKGROUND: Low back pain is the leading cause of disability worldwide with a significant socioeconomic burden; artificial intelligence (AI) has proved to have a great potential in supporting clinical decisions at each stage of the healthcare process...

Assessing the readability, quality and reliability of responses produced by ChatGPT, Gemini, and Perplexity regarding most frequently asked keywords about low back pain.

PeerJ
BACKGROUND: Patients who are informed about the causes, pathophysiology, treatment and prevention of a disease are better able to participate in treatment procedures in the event of illness. Artificial intelligence (AI), which has gained popularity i...

Assessing the performance of AI chatbots in answering patients' common questions about low back pain.

Annals of the rheumatic diseases
OBJECTIVES: The aim of this study was to assess the accuracy and readability of the answers generated by large language model (LLM)-chatbots to common patient questions about low back pain (LBP).

Use of machine learning to identify prognostic variables for outcomes in chronic low back pain treatment: a retrospective analysis.

The Journal of manual & manipulative therapy
OBJECTIVES: Most patients seen in physical therapy (PT) clinics for low back pain (LBP) are treated for chronic low back pain (CLBP), yet PT interventions suggest minimal effectiveness. The Cochrane Back Review Group proposed 'Holy Grail' questions, ...

An Efficient Muscle Segmentation Method via Bayesian Fusion of Probabilistic Shape Modeling and Deep Edge Detection.

IEEE transactions on bio-medical engineering
OBJECTIVE: Paraspinal muscle segmentation and reconstruction from MR images are critical to implement quantitative assessment of chronic and recurrent low back pains. Due to unclear muscle boundaries and shape variations, current segmentation methods...

Role of Artificial intelligence model in prediction of low back pain using T2 weighted MRI of Lumbar spine.

F1000Research
BACKGROUND: Low back pain (LBP), the primary cause of disability, is the most common musculoskeletal disorder globally and the primary cause of disability. Magnetic resonance imaging (MRI) studies are inconclusive and less sensitive for identifying a...

Lumbar Radicular Pain in the Eyes of Artificial Intelligence: Can You 'Imagine' What I 'Feel'?

World neurosurgery
OBJECTIVE: Pain is a complex sensory and emotional experience that significantly impacts individuals' well-being. Lumbar radicular pain (LRP) is a prevalent neuropathic pain affecting 9.9% to 25% of the population annually. Accurate identification of...

Machine learning in the diagnosis, management, and care of patients with low back pain: a scoping review of the literature and future directions.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Low back pain (LBP) remains the leading cause of disability globally. In recent years, machine learning (ML) has emerged as a potentially useful tool to aid the diagnosis, management, and prognostication of LBP.