The novel robot-assisted (RA) technique has been utilized increasingly to improve the accuracy of cervical pedicle screw placement. Although the clinical application of the RA technique has been investigated in several case series and comparative stu...
OBJECTIVE: A study of supervised automated classification of the cervical vertebrae maturation (CVM) stages using deep learning (DL) network is presented. A parallel structured deep convolutional neural network (CNN) with a pre-processing layer that ...
OBJECTIVES: This study aimed to automatically determine the cervical vertebral maturation (CVM) processes on lateral cephalometric radiograph images using a proposed deep learning-based convolutional neural network (CNN) model and to test the success...
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
Feb 6, 2023
PURPOSE: Postoperative complication prediction helps surgeons to inform and manage patient expectations. Deep learning, a model that finds patterns in large samples of data, outperform traditional statistical methods in making predictions. This study...
BACKGROUND: C1 lateral mass fractures (LMF) cause abnormal alignment of the upper cervical joints. Conservatively treated cases can develop into late cock-robin junction, requiring a reconstructive surgical procedure of the occipitocervical junction....
BACKGROUND: The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are few invest...
There is no consensus regarding the optimal treatment for unstable hangman's fractures. Conservative treatment, although time-consuming and associated with complications such as nonunion, neck stiffness, and pseudarthrosis formation, is still preferr...
OBJECTIVE: The aim of this study was to develop an artificial intelligence (AI) algorithm to automatically and accurately determine the stage of cervical vertebra maturation (CVM) with the main purpose being to eliminate the human error factor.
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