AIMC Topic: Spinal Diseases

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Preoperative prediction of postoperative urinary retention in lumbar surgery: a comparison of regression to multilayer neural network.

Journal of neurosurgery. Spine
OBJECTIVE: Postoperative urinary retention (POUR) is a common complication after spine surgery and is associated with prolongation of hospital stay, increased hospital cost, increased rate of urinary tract infection, bladder overdistention, and auton...

Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.

Neurosurgical review
Machine learning is a rapidly evolving field that offers physicians an innovative and comprehensive mechanism to examine various aspects of patient data. Cervical and lumbar degenerative spine disorders are commonly age-related disease processes that...

Artificial intelligence for adult spinal deformity: current state and future directions.

The spine journal : official journal of the North American Spine Society
As we experience a technological revolution unlike any other time in history, spinal surgery as a discipline is poised to undergo a dramatic transformation. As enormous amounts of data become digitized and more readily available, medical professional...

Pain phenotypes classified by machine learning using electroencephalography features.

NeuroImage
Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects ...

Initial classification of low back and leg pain based on objective functional testing: a pilot study of machine learning applied to diagnostics.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
OBJECTIVE: The five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment and thus provided an adjunctive dimension in patient assessment. The clinical interpretability and confounders of the 5R-STS remain poor...

Development of a machine learning algorithm for prediction of failure of nonoperative management in spinal epidural abscess.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Data regarding risk of failure of nonoperative management in spinal epidural abscess (SEA) are limited. Given the potential for deterioration with treatment failure, a tool that predicts the probability of failure would be of grea...

Bridging Health Literacy Gaps in Spine Care: Using ChatGPT-4o to Improve Patient-Education Materials.

The Journal of bone and joint surgery. American volume
BACKGROUND: Patient-education materials (PEMs) are essential to improve health literacy, engagement, and treatment adherence, yet many exceed the recommended readability levels. Therefore, individuals with limited health literacy are at a disadvantag...

Deep Learning Study of Alkaptonuria Spinal Disease Assesses Global and Regional Severity and Detects Occult Treatment Status.

Journal of inherited metabolic disease
Deep learning (DL) is increasingly used to analyze medical imaging, but is less refined for rare conditions, which require novel pre-processing and analytical approaches. To assess DL in the context of rare diseases, this study focused on alkaptonuri...

Artificial Intelligence in Spine Imaging: A Paradigm Shift in Diagnosis and Care.

Magnetic resonance imaging clinics of North America
Recent advancements in artificial intelligence (AI) can significantly improve radiologists' workflow, improving efficiency and diagnostic accuracy. Current AI applications within spine imaging are approved to accelerate image acquisition time, improv...