Analysis of Cervical Spinal MRI With Deep Learning

Cervical Disc Disease Clinical Trial

Official title:

Analysis of Cervical Spinal MRI With Deep Learning

Clinical Trial Details — Status: Withdrawn

Administrative data

• NCT number: NCT04239638
• Other study ID #: 54022451
• Status: Withdrawn
• Start date: January 15, 2020
• Est. completion date: April 1, 2022

Study information

• Verified date: July 2022
• Source: Bezmialem Vakif University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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. Finally; verification of the system by comparing radiologist reports and automated system outputs.

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.

Recruitment information / eligibility

• Status: Withdrawn
• Enrollment: 0
• Est. completion date: April 1, 2022
• Est. primary completion date: March 1, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 75 Years

Eligibility

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

Related Conditions & MeSH terms

• Cervical Disc Disease
• Cervical Spine Disease
• Spinal Diseases

Intervention

Diagnostic Test:
• Cervical Spinal MRI
Cervical Spinal MRI images of 500 patients will be entered into the system for modeling

Locations

Country	Name	City	State
Turkey	Bezmialem Vakif University Hospital	Istanbul

Sponsors (1)

Lead Sponsor	Collaborator
Bezmialem Vakif University

Country where clinical trial is conducted

Turkey, 

References & Publications (4)

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. — View Citation

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. — View Citation

Jamaludin A, Lootus M, Kadir T, Zisserman A, Urban J, Battié 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. — View Citation

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). pii: 1656. doi: 10.3390/app8091656. Epub 2018 Sep 14. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	Through study completion, an average of 1,5 years
Primary	Reliability of the model as assessed by comparing the reports of the model and radiologist.	Kappa statistics and reliability coefficients will be use.	Through study completion, an average of 1,5 years