AIMC Topic: Spinal Stenosis

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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.

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...

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).

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...

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...

Machine learning-based preoperative predictive analytics for lumbar spinal stenosis.

Neurosurgical focus
OBJECTIVEPatient-reported outcome measures (PROMs) following decompression surgery for lumbar spinal stenosis (LSS) demonstrate considerable heterogeneity. Individualized prediction tools can provide valuable insights for shared decision-making. The ...

Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning.

Neuroinformatics
Pathogenesis-based diagnosis is a key step to prevent and control lumbar neural foraminal stenosis (LNFS). It conducts both early diagnosis and comprehensive assessment by drawing crucial pathological links between pathogenic factors and LNFS. Automa...

Gait Analysis Using a Support Vector Machine for Lumbar Spinal Stenosis.

Orthopedics
Lumbar spinal canal stenosis (LSS) is diagnosed based on physical examination and radiological documentation of lumbar spinal canal narrowing. Differential diagnosis of the level of lumbar radiculopathy is difficult in multilevel spinal stenosis. The...

Rule-based Cervical Spine Defect Classification Using Medical Narratives.

Studies in health technology and informatics
Classifying the defects occurring at the cervical spine provides the basis for surgical treatment planning and therapy recommendation. This process requires evidence from patient records. Further, the degree of a defect needs to be encoded in a stand...