Cervical Disc DiseaseNCT04239638
Analysis of Cervical Spinal MRI With Deep Learning
An observational study of Cervical Disc Disease and Cervical Spine Disease, run by Bezmialem Vakif University at 1 site in Turkey (Türkiye).
Trial summary
The aim of this study is analyzing the pathologies in cervical spinal MRI images by using image processing algorithms. Determination of these pathological cases which taught to the system with deep learning and determination of their levels. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Cervical Disc Disease, Cervical Spine Disease
- What is being tested?
- Cervical Spinal MRI
- How many people
- 0 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 15 Jan 2020to 1 Apr 2022
- 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 18 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with neck pain between 18-75 years
Who may be able to join
- 18-75 years of age
- Having result of a cervical spinal MRI, which was performed for neck pain in the hospital records in the last 5 years.
Who cannot join
- Malignancy
- Signs of active infection
- Significant spinal vertebral deformity (advanced scoliosis, congenital vertebral defects)
- Spinal surgery
Trial locations and contacts
1 site in 1 country.
About this study
The aim of this study is analyzing the pathologies in cervical spinal MRI images by using image processing algorithms. Determination of these pathological cases which taught to the system with deep learning and determination of their levels. Finally; verification of the system by comparing radiologist reports and automated system outputs.
Read the detailed description
Neck pain is a very common health problem with a worldwide prevalence ranging from 16.7% to 75.1%. The source of neck pain is often considered - although there is no strong evidence - the cervical intervertebral disc. Radiological imaging methods are used for the detection of degeneration of the discs and the end plaque changes in the vertebral body corresponding to this degeneration.Magnetic Resonance Imaging (MRI) gives information about the structure of intervertebral disc, width of spinal canal and tissues outside the canal. However, there is no standardization in the identification and evaluation of radiological images, and interobserver variability is high. Studies have been initiated on automated systems that analyze MRI images to increase the accuracy and consistency of reporting procedures. Examining MRI images with deep learning can lead to the production of systems that help clinical decision making and also allows the evaluation of large data in a short time.
- Conditions
- Cervical Disc Disease, Cervical Spine Disease
- Being tested
- Cervical Spinal MRI
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Cervical Spinal MRIDiagnostic test
Cervical Spinal MRI images of 500 patients will be entered into the system for modeling
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy rate of the model as assessed by cross validation of the data set
We will randomly divide the dataset into 4 subsets. In each sub-experiments, MRI slices from 3 subsets will be trained and slices in the other subset will be tested. We will perform totally 4 sub-experiments, so each slice in the dataset will be tested once.
Measured: Through study completion, an average of 1,5 years
Reliability of the model as assessed by comparing the reports of the model and radiologist.
Kappa statistics and reliability coefficients will be use.
Measured: Through study completion, an average of 1,5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jan 2020
- Study start15 Jan 2020
- Primary completion1 Mar 2022 (estimated)
- Study completion1 Apr 2022 (estimated)
Why it stopped: postponed to a later date
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Cervical Disc DiseaseStudies of the same condition, open and under way.
- 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Bezmialem Vakif University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Bugra Ince, MD, Principal investigator, Bezmialem Vakif University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04239638
- Sponsor's protocol ID
- 54022451
- Status last verified
- Jul 2022
- First submitted
- 15 Jan 2020
- Last updated
- 21 Jul 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Castro-Mateos I, Hua R, Pozo JM, Lazary A, Frangi AF. Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images. Eur Spine J. 2016 Sep;25(9):2721-7. doi: 10.1007/s00586-016-4654-6. Epub 2016 Jul 7.
PubMed 27388019Jamaludin A, Lootus M, Kadir T, Zisserman A, Urban J, Battie MC, Fairbank J, McCall I; Genodisc Consortium. ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist. Eur Spine J. 2017 May;26(5):1374-1383. doi: 10.1007/s00586-017-4956-3. Epub 2017 Feb 6.
PubMed 28168339Kim S, Bae WC, Masuda K, Chung CB, Hwang D. Fine-Grain Segmentation of the Intervertebral Discs from MR Spine Images Using Deep Convolutional Neural Networks: BSU-Net. Appl Sci (Basel). 2018 Sep;8(9):1656. doi: 10.3390/app8091656. Epub 2018 Sep 14.
PubMed 30637135Daenzer S, Freitag S, von Sachsen S, Steinke H, Groll M, Meixensberger J, Leimert M. VolHOG: a volumetric object recognition approach based on bivariate histograms of oriented gradients for vertebra detection in cervical spine MRI. Med Phys. 2014 Aug;41(8):082305. doi: 10.1118/1.4890587.
PubMed 25086554
Study at a glance
From the registry record, last updated by the sponsor on 21 Jul 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Analysis of Cervical Spinal MRI With Deep Learning
- ClinicalTrials.gov ID
- NCT04239638
- Acronym / sponsor ID
- 54022451
- Conditions
- Cervical Disc Disease, Cervical Spine Disease
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: postponed to a later date
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Bezmialem Vakif UniversityUniversity, hospital or other
- Study start
- 15 Jan 2020 (actual)
- Primary completion
- 1 Mar 2022 (estimated)
- Study completion
- 1 Apr 2022 (estimated)
- First posted
- 27 Jan 2020 (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.
The aim of this study is analyzing the pathologies in cervical spinal MRI images by using image processing algorithms. Determination of these pathological cases which taught to the system with deep learning and determination of their levels. Finally; verification of the system by comparing radiologist reports and automated system outputs.
Detailed description
Neck pain is a very common health problem with a worldwide prevalence ranging from 16.7% to 75.1%. The source of neck pain is often considered - although there is no strong evidence - the cervical intervertebral disc. Radiological imaging methods are used for the detection of degeneration of the discs and the end plaque changes in the vertebral body corresponding to this degeneration.Magnetic Resonance Imaging (MRI) gives information about the structure of intervertebral disc, width of spinal canal and tissues outside the canal. However, there is no standardization in the identification and evaluation of radiological images, and interobserver variability is high. Studies have been initiated on automated systems that analyze MRI images to increase the accuracy and consistency of reporting procedures. Examining MRI images with deep learning can lead to the production of systems that help clinical decision making and also allows the evaluation of large data in a short time.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with neck pain between 18-75 years
Key inclusion criteria (2 listed)
- 18-75 years of age
- Having result of a cervical spinal MRI, which was performed for neck pain in the hospital records in the last 5 years.
Key exclusion criteria (4 listed)
- Malignancy
- Signs of active infection
- Significant spinal vertebral deformity (advanced scoliosis, congenital vertebral defects)
- Spinal surgery
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 |
|---|---|---|
| Cervical Spinal MRICervical Spinal MRI images of 500 patients will be entered into the system for modeling | Diagnostic test | – |
Primary outcome
Accuracy rate of the model as assessed by cross validation of the data set
Time frame: Through study completion, an average of 1,5 years
We will randomly divide the dataset into 4 subsets. In each sub-experiments, MRI slices from 3 subsets will be trained and slices in the other subset will be tested. We will perform totally 4 sub-experiments, so each slice in the dataset will be tested once.
Reliability of the model as assessed by comparing the reports of the model and radiologist.
Time frame: Through study completion, an average of 1,5 years
Kappa statistics and reliability coefficients will be use.
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Jul 2022.
- Background: PubMed 27388019Castro-Mateos I, Hua R, Pozo JM, Lazary A, Frangi AF. Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images. Eur Spine J. 2016 Sep;25(9):2721-7. doi: 10.1007/s00586-016-4654-6. Epub 2016 Jul 7.
- Background: PubMed 28168339Jamaludin A, Lootus M, Kadir T, Zisserman A, Urban J, Battie MC, Fairbank J, McCall I; Genodisc Consortium. ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist. Eur Spine J. 2017 May;26(5):1374-1383. doi: 10.1007/s00586-017-4956-3. Epub 2017 Feb 6.
- Background: PubMed 30637135Kim S, Bae WC, Masuda K, Chung CB, Hwang D. Fine-Grain Segmentation of the Intervertebral Discs from MR Spine Images Using Deep Convolutional Neural Networks: BSU-Net. Appl Sci (Basel). 2018 Sep;8(9):1656. doi: 10.3390/app8091656. Epub 2018 Sep 14.
- Background: PubMed 25086554Daenzer S, Freitag S, von Sachsen S, Steinke H, Groll M, Meixensberger J, Leimert M. VolHOG: a volumetric object recognition approach based on bivariate histograms of oriented gradients for vertebra detection in cervical spine MRI. Med Phys. 2014 Aug;41(8):082305. doi: 10.1118/1.4890587.
- Individual participant dataSharing planned: Undecided
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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.
Bugra Ince, MD principal investigator
Bezmialem Vakif University
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 Disc Disease and Cervical Spine Disease, run by Bezmialem Vakif 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 4 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: withdrawn, as of the sponsor's last update on 21 Jul 2022. 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 was withdrawn before enrolling participants, so there are no results.
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?
Accuracy rate of the model as assessed by cross validation of the data set (time frame: Through study completion, an average of 1,5 years). 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?
On the ClinicalTrials.gov record for NCT04239638, which also lists 4 linked publications.
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 21 Jul 2022.