AIMC Topic: Cervical Vertebrae

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An efficient deep learning based approach for automated identification of cervical vertebrae fracture as a clinical support aid.

Scientific reports
Cervical vertebrae fractures pose a significant risk to a patient's health. The accurate diagnosis and prompt treatment need to be provided for effective treatment. Moreover, the automated analysis of the cervical vertebrae fracture is of utmost impo...

Deep learning algorithm for identifying osteopenia/osteoporosis using cervical radiography.

Scientific reports
Due to symptomatic gait imbalance and a high incidence of falls, patients with cervical disease-including degenerative cervical myelopathy-have a significantly increased risk of fragility fractures. To prevent such fractures in patients with cervical...

Multi-modal and Multi-view Cervical Spondylosis Imaging Dataset.

Scientific data
Multi-modal and multi-view imaging is essential for diagnosis and assessment of cervical spondylosis. Deep learning has increasingly been developed to assist in diagnosis and assessment, which can help improve clinical management and provide new idea...

C2 pars interarticularis length on the side of high-riding vertebral artery with implications for pars screw insertion.

Scientific reports
C2 pars interarticularis length (C2PIL) required for pars screws has not been thoroughly studied in subjects with high-riding vertebral artery (HRVA). We aimed to measure C2PIL specifically on the sides with HRVA, define short pars, optimal pars scre...

Torg-Pavlov ratio qualification to diagnose developmental cervical spinal stenosis based on HRViT neural network.

BMC musculoskeletal disorders
BACKGROUND: Developing computer-assisted methods to measure the Torg-Pavlov ratio (TPR), defined as the ratio of the sagittal diameter of the cervical spinal canal to the sagittal diameter of the corresponding vertebral body on lateral radiographs, c...

Deep Learning-Based Reconstruction for Accelerated Cervical Spine MRI: Utility in the Evaluation of Myelopathy and Degenerative Diseases.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Deep learning (DL)-based reconstruction enables improving the quality of MR images acquired with a short scan time. We aimed to prospectively compare the image quality and diagnostic performance in evaluating cervical degenera...

Deep learning based quantitative cervical vertebral maturation analysis.

Head & face medicine
OBJECTIVES: This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives ...

A stacking ensemble machine learning model for predicting postoperative axial pain intensity in patients with degenerative cervical myelopathy.

Scientific reports
Machine learning (ML) has been extensively utilized to predict complications associated with various diseases. This study aimed to develop ML-based classifiers employing a stacking ensemble strategy to forecast the intensity of postoperative axial pa...

Establishment and validation of a ResNet-based radiomics model for predicting prognosis in cervical spinal cord injury patients.

Scientific reports
Cervical spinal cord injury (cSCI) poses a significant challenge due to the unpredictable nature of recovery, which ranges from mild paralysis to severe long-term disability. Accurate prognostic models are crucial for guiding treatment and rehabilita...

The Role of Claude 3.5 Sonet and ChatGPT-4 in Posterior Cervical Fusion Patient Guidance.

World neurosurgery
BACKGROUND: This study evaluates the role of ChatGPT-4 and Claude 3.5 Sonet in postoperative management for patients undergoing posterior cervical fusion. It focuses on their ability to provide accurate, clear, and relevant responses to patient conce...