Analysis of Cervical Spinal MRI With Deep Learning
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Contacts and Locations
Study Locations
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Istanbul, Turkey
- Bezmialem Vakif University Hospital
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- 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.
Exclusion Criteria:
- Malignancy
- Signs of active infection
- Significant spinal vertebral deformity (advanced scoliosis, congenital vertebral defects)
- Spinal surgery
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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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
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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.
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Through study completion, an average of 1,5 years
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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.
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Through study completion, an average of 1,5 years
|
Collaborators and Investigators
Sponsor
Sponsor
Publications and helpful links
General 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.
- Jamaludin 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.
- Kim 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.
- Daenzer 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.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Anticipated)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 54022451
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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