Clinical trial NCT04796987: Convolutional Neural Network for the Detection of Cervical Myelomalacia
Official title: Convolutional Neural Network for the Detection of Cervical Myelomalacia on Magnetic Resonance Imaging
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Cervical Myelopathy
Keywords: neural network
deep learning
cervical myelopathy
Lead sponsor: Istanbul University
Sponsor class: OTHER
First posted: 2021-03-15
Enrollment: 125
Sex: ALL
Minimum age (years): 32
Maximum age (years): 77
Standard ages: ADULT
OLDER_ADULT
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: The participants are aged 30-80 years, who have cervical myelomalacia that proved in MRI.

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.

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) .

Primary outcomes:
measure: The value of confusion matrix accuracy for sagittal views
timeFrame: 1 day
measure: The value of confusion matrix accuracy for axial views
timeFrame: 1 day
Secondary outcomes:
Other outcomes:
Locations:
city: Istanbul
country: Turkey
facility: İstanbul University
state: Fatih
Study officials:
affiliation: Karabuk University, Faculty of Engineering
name: Hakan Yilmaz
role: PRINCIPAL_INVESTIGATOR
affiliation: Istanbul University, Faculty of Medicine
name: Murat Korkmaz
role: PRINCIPAL_INVESTIGATOR
References: