Analysis of Cervical Spinal MRI With Deep Learning

NCT ID: NCT04239638

Last Updated: 2022-07-21

Basic Information

• Recruitment Status: WITHDRAWN
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-01-15
• Study Completion Date: 2022-04-01

Brief 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.

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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Interventions

Cervical Spinal MRI

Cervical Spinal MRI images of 500 patients will be entered into the system for modeling

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Bezmialem Vakif University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Bugra Ince, MD

Role: PRINCIPAL_INVESTIGATOR

Bezmialem Vakif University

Locations

Bezmialem Vakif University Hospital

Istanbul, , Turkey (Türkiye)

Countries

Turkey (Türkiye)

References

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.

Reference Type: BACKGROUND

PMID: 27388019 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 28168339 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 30637135 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 25086554 (View on PubMed)

Other Identifiers

54022451

Identifier Type: -

Identifier Source: org_study_id