Cervical Myelopathy
Conditions
Keywords
neural network, deep learning, cervical myelopathy
Brief summary
Deep learning technology has been used increasingly in spine surgery as well as in many medical fields. However, it is noticed that most of the studies about this subject in the literature have been conducted except of the cervical spine. In this study, we aimed to demonstrate the effectiveness of the deep learning algorithm in the diagnosis of cervical myelomalacia compared to conventional diagnostic methods. Artificial neural networks, a machine learning technique, have been used in several industrial and research fields increasingly. The development of computational units and the increasing amount of data led to the development of new methods on artificial neural networks
Detailed description
Cervical myelopathy (CM) is a frequent degenerative disease of the cervical spine that occurs as a result of compression of the spinal cord. In evaluating of this disease and determining treatment options, the patient's clinic and radiological modalities should be evaluated together. The current imaging procedures for CM are plain roentgenograms, computed tomography and magnetic resonance imaging (MRI). However, MRI in CM is more valuable in evaluating of the disc, spinal cord and other soft tissues compared to other imaging methods. Artificial intelligence technologies also used in many health applications such as medical image analysis, biological signal analysis, etc. In this study, we aimed to demonstrate the effectiveness of the deep learning algorithm in the diagnosis of cervical myelomalacia compared to conventional diagnostic methods.
Interventions
DIAGNOSTIC_TESTConvolutional Neural Network
Convolutional neural networks, a machine learning technique, have been used in several industrial and research fields increasingly. The development of computational units and the increasing amount of data led to the development of new methods on artificial neural networks. Deep learning (DL) is a multi-layered neural network in which feature extraction is done automatically. It extends traditional neural networks by adding more hidden layers to the network architecture between the input and output layers to model more complex and nonlinear relationships.
Sponsors
Istanbul University
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
32 Years to 77 Years
Inclusion criteria
* the patients with classical cervical myelomalacia sypmtoms such as neck pain and stiffness, weakness and clumsiness at the upper extremities or gait difficulties and radiological findings of spinal compression * 30-80 years age.
Exclusion criteria
* Patients with a previous history of cervical spinal surgery and has a systematic disease (rheumatologic or neural disease) .
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The value of confusion matrix accuracy for sagittal views | 1 day | It is a specific table layout that allows visualization of the performance of an algorithm. |
| The value of confusion matrix accuracy for axial views | 1 day | It is a specific table layout that allows visualization of the performance of an algorithm. |
Countries
Turkey (Türkiye)
Outcome results
None listed