Convolutional Neural Network for the Detection of Cervical Myelomalacia

Cervical Myelopathy Clinical Trial

Official title:

Convolutional Neural Network for the Detection of Cervical Myelomalacia on Magnetic Resonance Imaging

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04796987
• Other study ID #: KAEK/2020.07.129
• Status: Completed
• Start date: April 15, 2021
• Est. completion date: April 22, 2021

Study information

• Verified date: May 2021
• Source: Istanbul University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 125
• Est. completion date: April 22, 2021
• Est. primary completion date: April 22, 2021
• Gender: All
• Age group: 32 Years to 77 Years

Eligibility

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) .

Related Conditions & MeSH terms

• Bone Marrow Diseases
• Cervical Myelopathy
• Spinal Cord Diseases

Intervention

Diagnostic Test:
• Convolutional 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.

Locations

Country	Name	City	State
Turkey	Istanbul University	Istanbul	Fatih

Sponsors (1)

Lead Sponsor	Collaborator
Istanbul University

Country where clinical trial is conducted

Turkey, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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
Primary	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