AIMC Topic: Spinal Cord Compression
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Deep Learning-Based Segmentation of Cervical Posterior Longitudinal Ligament Ossification in Computed Tomography Images and Assessment of Spinal Cord Compression: A Two-Center Study.
World neurosurgery
Jan 9, 2025
OBJECTIVE: This study aims to develop a fully automated, computed tomography (CT)-based deep learning (DL) model to segment ossified lesions of the posterior longitudinal ligament and to measure the thickness of the ossified material and calculate th...
Detection of Vertebral Mass and Diagnosis of Spinal Cord Compression in Computed Tomography With Deep Learning Reconstruction: Comparison With Hybrid Iterative Reconstruction.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Oct 5, 2023
PURPOSE: To compare the impact of deep learning reconstruction (DLR) and hybrid-iterative reconstruction (hybrid-IR) on vertebral mass depiction, detection, and diagnosis of spinal cord compression on computed tomography (CT).
A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT.
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
Apr 24, 2023
PURPOSE: To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians.
Deep Learning Algorithm for Identifying Cervical Cord Compression Due to Degenerative Canal Stenosis on Radiography.
Spine
Feb 10, 2023
STUDY DESIGN: Cross-sectional study.
A deep learning model for detection of cervical spinal cord compression in MRI scans.
Scientific reports
May 18, 2021
Magnetic Resonance Imaging (MRI) evidence of spinal cord compression plays a central role in the diagnosis of degenerative cervical myelopathy (DCM). There is growing recognition that deep learning models may assist in addressing the increasing volum...
Machine Learning for the Prediction of Cervical Spondylotic Myelopathy: A Post Hoc Pilot Study of 28 Participants.
World neurosurgery
Mar 25, 2019
BACKGROUND: Cervical spondylotic myelopathy (CSM) severity and presence of symptoms are often difficult to predict based simply on clinical imaging alone. Similarly, improved machine learning techniques provide new tools with immense clinical potenti...
Shared Decision-Making Ontology for a Healthcare Team Executing a Workflow, an Instantiation for Metastatic Spinal Cord Compression Management.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Dec 5, 2018
Regardless of potential benefits and better outcomes, adoption of shared decision-making between a patient and providers involved in his/her care is still in its infancy. This paper intends to fill this gap by formalizing shared decision-making, situ...
Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care.
World neurosurgery
Aug 1, 2020
Personalized medicine is a new paradigm of healthcare in which interventions are based on individual patient characteristics rather than on "one-size-fits-all" guidelines. As epidemiological datasets continue to burgeon in size and complexity, powerf...
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