Convolutional Neural Network for the Detection of Cervical Myelomalacia

Completed

• Conditions: Cervical Myelopathy
• Interventions: Diagnostic Test: Convolutional Neural Network

• Registration Number: NCT04796987
• Lead Sponsor: Istanbul University

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.

Study Timeline

• Study First Submitted: 2021-03-11
• Study First Submitted QC: 2021-03-11
• Study First Posted: 2021-03-15
• Study Start: 2021-04-15
• Primary Completion: 2021-04-22
• Study Completion: 2021-04-22
• Status Verified: 2021-05
• Last Update Submitted: 2021-05-27
• Last Update Posted: 2021-06-01

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 125

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) .

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
normal	Convolutional Neural Network	normal section of the MRI of patients with cervical myelopathy
cervical myelopathy	Convolutional Neural Network	MR images of patients with cervical myelopathy

Primary Outcome Measures

Name	Time	Method
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.

Trial Locations

Locations (1)

İstanbul University; 🇹🇷 Istanbul, Fatih, Turkey