Convolutional Neural Network for the Detection of Cervical Myelomalacia

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

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Key information

Conditions

Cervical Myelopathy

Age range

32 year–77 year

Sex eligibility

All sexes

Study type

Observational

Primary location

İstanbul University

Istanbul, Fatih, 34093, Turkey (Türkiye)

About this study

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.

Who can participate

Only the study team can determine whether someone qualifies for participation.

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) .

Treatment and study plan

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.

Primary outcomes

The value of confusion matrix accuracy for sagittal views

Time frame: 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

Time frame: 1 day

It is a specific table layout that allows visualization of the performance of an algorithm.

Sponsors and collaborators

Lead sponsor

Istanbul University

Other

Registry information

Official study title

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

Important dates

Study start: 2021
Primary completion: 2021
Study completion: 2021
First posted: Mar 15, 2021
Registry last updated: Jun 1, 2021

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed