Convolutional Neural Network for the Detection of Cervical Myelomalacia

NCT ID: NCT04796987

Last Updated: 2021-06-01

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 125 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-04-15
• Study Completion Date: 2021-04-22

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.

Conditions

Cervical Myelopathy

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: RETROSPECTIVE

Study Groups

cervical myelopathy

MR images of patients with cervical myelopathy

Convolutional Neural Network

Intervention Type: 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.

normal

normal section of the MRI of patients with cervical myelopathy

Convolutional Neural Network

Intervention Type: 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.

Interventions

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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) .

• Minimum Eligible Age: 32 Years
• Maximum Eligible Age: 77 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Istanbul University

OTHER

Sponsor Role: lead

Responsible Party

Merve Damla Korkmaz

Principle investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Hakan Yilmaz

Role: PRINCIPAL_INVESTIGATOR

Karabuk University, Faculty of Engineering

Murat Korkmaz

Role: PRINCIPAL_INVESTIGATOR

Istanbul University, Faculty of Medicine

Locations

İstanbul University

Istanbul, Fatih, Turkey (Türkiye)

Countries

Turkey (Türkiye)

Other Identifiers

KAEK/2020.07.129

Identifier Type: -

Identifier Source: org_study_id