Cervical MyelopathyNCT04796987
Convolutional Neural Network for the Detection of Cervical Myelomalacia
An observational study of Cervical Myelopathy, run by Istanbul University at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 32 to 77 years
- Conditions the study is for
- Cervical Myelopathy
- What is being tested?
- Convolutional Neural Network
- How many people
- 125 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 15 Apr 2021to 22 Apr 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 32 to 77 years
- All sexes
- Adult, Older adult
Study population: The participants are aged 30-80 years, who have cervical myelomalacia that proved in MRI.
Who may be able to join
- 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.
Who cannot join
- Patients with a previous history of cervical spinal surgery and has a systematic disease (rheumatologic or neural disease) .
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Convolutional Neural Network for the Detection of Cervical Myelomalacia on Magnetic Resonance Imaging
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
Read the 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.
- Conditions
- Cervical Myelopathy
- Being tested
- Convolutional Neural Network
- Keywords
- neural network, deep learning, cervical myelopathy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Convolutional Neural NetworkDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| cervical myelopathy | MR images of patients with cervical myelopathy Receives: Diagnostic Test: Convolutional Neural Network | |
| normal | normal section of the MRI of patients with cervical myelopathy Receives: Diagnostic Test: Convolutional Neural Network |
What is being measured
The primary outcome is the main question the study is designed to answer.
The value of confusion matrix accuracy for sagittal views
It is a specific table layout that allows visualization of the performance of an algorithm.
Measured: 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.
Measured: 1 day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Mar 2021
- Study start15 Apr 2021
- Primary completion22 Apr 2021
- Study completion22 Apr 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cervical MyelopathyStudies of the same condition, open and under way.
- More trials run by Istanbul UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 125 participants (actual)
Sponsor and investigators
- Lead sponsor
- Istanbul University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Merve Damla Korkmaz, Istanbul University
- Investigators
- Hakan Yilmaz, Principal investigator, Karabuk University, Faculty of EngineeringMurat Korkmaz, Principal investigator, Istanbul University, Faculty of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04796987
- Sponsor's protocol ID
- KAEK/2020.07.129
- Status last verified
- May 2021
- First submitted
- 11 Mar 2021
- Last updated
- 1 Jun 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: It can be shared after publication
Study at a glance
From the registry record, last updated by the sponsor on 1 Jun 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Convolutional Neural Network for the Detection of Cervical Myelomalacia on Magnetic Resonance Imaging
- ClinicalTrials.gov ID
- NCT04796987
- Acronym / sponsor ID
- KAEK/2020.07.129
- Conditions
- Cervical Myelopathy
- Keywords
- neural network, deep learning, cervical myelopathy
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 125 participants (actual)
- Lead sponsor
- Istanbul UniversityUniversity, hospital or other
- Study start
- 15 Apr 2021 (actual)
- Primary completion
- 22 Apr 2021 (actual)
- Study completion
- 22 Apr 2021 (actual)
- First posted
- 15 Mar 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| cervical myelopathy | – | Diagnostic Test: Convolutional Neural NetworkMR images of patients with cervical myelopathy |
| normal | – | Diagnostic Test: Convolutional Neural Networknormal section of the MRI of patients with cervical myelopathy |
Who may qualify
- Ages
- 32 to 77 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- The participants are aged 30-80 years, who have cervical myelomalacia that proved in MRI.
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (1 listed)
- Patients with a previous history of cervical spinal surgery and has a systematic disease (rheumatologic or neural disease) .
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Convolutional Neural NetworkConvolutional 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. | Diagnostic test | cervical myelopathy, normal |
Primary outcome
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Jun 2021.
- Individual participant dataSharing planned: Yes. It can be shared after publication
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Hakan Yilmaz principal investigator
Karabuk University, Faculty of Engineering
Murat Korkmaz principal investigator
Istanbul University, Faculty of Medicine
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cervical Myelopathy, run by Istanbul University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 1 Jun 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The value of confusion matrix accuracy for sagittal views (time frame: 1 day). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Jun 2021.