Convolutional Neural Network for the Detection of Cervical Myelomalacia

Istanbul University125 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

125

Conditions

Cervical Myelopathy

Interventions

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.

Eligibility

Sex: ALLMin age: 32 YearsMax age: 77 Years

Medical Language ↔ Plain English

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) .

Outcomes

Primary Outcomes

The value of confusion matrix accuracy for sagittal views

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

Time frame: 1 day

The value of confusion matrix accuracy for axial views