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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
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| cervical myelopathy | MR images of patients with cervical myelopathy |
| |
| normal | normal section of the MRI of patients with cervical myelopathy |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Convolutional Neural Network | Diagnostic Test | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| The value of confusion matrix accuracy for sagittal views | It is a specific table layout that allows visualization of the performance of an algorithm. | 1 day |
| The value of confusion matrix accuracy for axial views | It is a specific table layout that allows visualization of the performance of an algorithm. | 1 day |
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Inclusion Criteria:
Exclusion Criteria:
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The participants are aged 30-80 years, who have cervical myelomalacia that proved in MRI.
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| Name | Affiliation | Role |
|---|---|---|
| Hakan Yilmaz | Karabuk University, Faculty of Engineering | Principal Investigator |
| Murat Korkmaz | Istanbul University, Faculty of Medicine | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| İstanbul University | Istanbul | Fatih | 34093 | Turkey (Türkiye) |
It can be shared after publication
after publication
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| ID | Term |
|---|---|
| D000098415 | Convolutional Neural Networks |
| ID | Term |
|---|---|
| D016571 | Neural Networks, Computer |
| D055641 | Mathematical Concepts |
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