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Lumbar Vertebrae

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Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach.

World neurosurgery
BACKGROUND: Given the significant cost and morbidity of patients undergoing lumbar fusion, accurate preoperative risk-stratification would be of great utility. We aim to develop a machine learning model for prediction of major complications and readm...

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...

Accuracy and deviation analysis of robot-assisted spinal implants: A retrospective overview of 105 cases and preliminary comparison to open freehand surgery in lumbar spondylolisthesis.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Whether the accuracy of robot-assisted spinal screw placement is significantly higher than that of freehand and the source of robotic deviation remain unclear.

Robot-assisted minimally invasive transforaminal lumbar interbody fusion versus open transforaminal lumbar interbody fusion: a retrospective matched-control analysis for clinical and quality-of-life outcomes.

Journal of comparative effectiveness research
To compare the screw accuracy and clinical outcomes between robot-assisted minimally invasive transforaminal lumbar interbody fusion (RA MIS-TLIF) and open TLIF in the treatment of one-level lumbar degenerative disease. From May 2018 to December 20...

Predicting Readmission After Anterior, Posterior, and Posterior Interbody Lumbar Spinal Fusion: A Neural Network Machine Learning Approach.

World neurosurgery
BACKGROUND: Readmission after spine surgery is costly and a relatively common occurrence. Previous research identified several risk factors for readmission; however, the conclusions remain equivocal. Machine learning algorithms offer a unique perspec...

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...

Does Robot Navigation and Intraoperative Computed Tomography Guidance Help with Percutaneous Endoscopic Lumbar Discectomy? A Match-Paired Study.

World neurosurgery
OBJECTIVE: To evaluate the efficacy and safety of robot-assisted percutaneous endoscopic lumbar discectomy (rPELD) using a specially designed orthopaedic robot with an intraoperative computed tomography-equipped suite for treatment of symptomatic lum...

Predictive Model for Selection of Upper Treated Vertebra Using a Machine Learning Approach.

World neurosurgery
OBJECTIVE: To train and validate an algorithm mimicking decision making of experienced surgeons regarding upper instrumented vertebra (UIV) selection in surgical correction of thoracolumbar adult spinal deformity.

Comparison of robot-assisted and freehand pedicle screw placement for lumbar revision surgery.

International orthopaedics
BACKGROUND: The accuracy of robot-assisted pedicle screw implantation is a safe and effective method in lumbar surgery, but it still remains controversial in lumbar revision surgery. This study evaluated the clinical safety and accuracy of robot-assi...