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Convolutional Neural Network for the Detection of Cervical Myelomalacia

NCT04796987 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 125

Last updated 2021-06-01

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

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.

Sponsors & Collaborators

  • Istanbul University

    lead OTHER

Principal Investigators

  • Hakan Yilmaz · Karabuk University, Faculty of Engineering

  • Murat Korkmaz · Istanbul University, Faculty of Medicine

Eligibility

Min Age
32 Years
Max Age
77 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-04-15
Primary Completion
2021-04-22
Completion
2021-04-22

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04796987 on ClinicalTrials.gov