Convolutional Neural Network for the Detection of Cervical Myelomalacia

NCT04796987 | Completed

• 1 other identifier: KAEK/2020.07.129
• Study Type: observational
• Target: 125
• Locations: 1 country
• Sites: 1

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

Conditions

Cervical Myelopathy

Keywords

neural network; deep learning; cervical myelopathy

Outcome Measures

Primary Outcomes (2)

• The value of confusion matrix accuracy for sagittal views

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

  1 day

• The value of confusion matrix accuracy for axial views

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

  1 day

Study Arms (2)

cervical myelopathy

MR images of patients with cervical myelopathy

Diagnostic Test: Convolutional Neural Network

normal

normal section of the MRI of patients with cervical myelopathy

Diagnostic Test: Convolutional Neural Network

Interventions

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.

cervical myelopathy; normal

Eligibility Criteria

• Age: 32 Years - 77 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The participants are aged 30-80 years, who have cervical myelomalacia that proved in MRI.

You may qualify if:

• 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
• years age.

You may not qualify if:

• Patients with a previous history of cervical spinal surgery and has a systematic disease (rheumatologic or neural disease) .

Sponsors & Collaborators

• Istanbul University: lead

Study Sites (1)

İstanbul University

Istanbul, Fatih, 34093, Turkey (Türkiye)

Location

MeSH Terms

Interventions

Convolutional Neural Networks

Intervention Hierarchy (Ancestors)

Neural Networks, Computer; Mathematical Concepts

Study Officials

• Hakan Yilmaz

  Karabuk University, Faculty of Engineering

  PRINCIPAL INVESTIGATOR

• Murat Korkmaz

  Istanbul University, Faculty of Medicine

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principle investigator

Study Record Dates

• First Submitted: March 11, 2021
• First Posted: March 15, 2021
• Study Start: April 15, 2021
• Primary Completion: April 22, 2021
• Study Completion: April 22, 2021
• Last Updated: June 1, 2021

Record last verified: 2021-05

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF
• Time Frame: after publication

Locations

TU (1)