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Spinal Stenosis

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Artificial Intelligence for the Treatment of Lumbar Spondylolisthesis.

Neurosurgery clinics of North America
Multiple registries are currently collecting patient-specific data on lumbar spondylolisthesis including outcomes data. The collection of imaging diagnostics data along with comparative outcomes data following decompression versus decompression and f...

Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Quantitative imaging biomarkers have not been established for the diagnosis of spinal canal stenosis. This work aimed to lay the groundwork to establish such biomarkers by leveraging the developments in machine learning and me...

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...

Could automated machine-learned MRI grading aid epidemiological studies of lumbar spinal stenosis? Validation within the Wakayama spine study.

BMC musculoskeletal disorders
BACKGROUND: MRI scanning has revolutionized the clinical diagnosis of lumbar spinal stenosis (LSS). However, there is currently no consensus as to how best to classify MRI findings which has hampered the development of robust longitudinal epidemiolog...

Spinal Stenosis Grading in Magnetic Resonance Imaging Using Deep Convolutional Neural Networks.

Spine
STUDY DESIGN: Retrospective magnetic resonance imaging grading with comparison between experts and deep convolutional neural networks (CNNs).

Incorporating New Technologies to Overcome the Limitations of Endoscopic Spine Surgery: Navigation, Robotics, and Visualization.

World neurosurgery
Recently, spine surgery has gradually evolved from conventional open surgery to minimally invasive surgery, and endoscopic spine surgery (ESS) has become an important procedure in minimally invasive spine surgery. With improvements in the optics, spi...

Effects of age and sex on the distribution and symmetry of lumbar spinal and neural foraminal stenosis: a natural language processing analysis of 43,255 lumbar MRI reports.

Neuroradiology
PURPOSE: The purpose of this study is to investigate relationship of patient age and sex to patterns of degenerative spinal stenosis on lumbar MRI (LMRI), rated as moderate or greater by a spine radiologist, using natural language processing (NLP) to...

Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI.

Radiology
Background Assessment of lumbar spinal stenosis at MRI is repetitive and time consuming. Deep learning (DL) could improve -productivity and the consistency of reporting. Purpose To develop a DL model for automated detection and classification of lumb...

Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning.

Neurosurgery
BACKGROUND: The referral process for consultation with a spine surgeon remains inefficient, given a substantial proportion of referrals to spine surgeons are nonoperative.

Sequential endoscopic and robot-assisted surgical solutions for a rare fungal spondylodiscitis, secondary lumbar spinal stenosis, and subsequent discal pseudocyst causing acute cauda equina syndrome: a case report.

BMC surgery
BACKGROUND: Fungal spondylodiscitis is a rare infectious disease. The secondary lumbar spinal stenosis and postoperative discal pseudocyst were even rarer. The surgical interventions were disputed, yet endoscopic and robot-assisted techniques may be ...