Analysis of Cervical Spinal MRI With Deep Learning

NCT04239638 | Withdrawn

• 1 other identifier: 54022451
• Study Type: observational
• Target: N/A
• Locations: 1 country
• Sites: 1

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.

Conditions

Cervical Disc Disease; Cervical Spine Disease

Outcome Measures

Primary Outcomes (2)

• 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

• 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

Interventions

Cervical Spinal MRI DIAGNOSTIC_TEST

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

Eligibility Criteria

• Age: 18 Years - 75 Years
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with neck pain between 18-75 years

You may qualify if:

• 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.

You may not qualify if:

• Malignancy
• Signs of active infection
• Significant spinal vertebral deformity (advanced scoliosis, congenital vertebral defects)
• Spinal surgery

Sponsors & Collaborators

• Bezmialem Vakif University: lead

Study Sites (1)

Bezmialem Vakif University Hospital

Istanbul, Turkey (Türkiye)

Location

Related 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.

  PMID: 27388019 BACKGROUND

• 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.

  PMID: 28168339 BACKGROUND

• 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.

  PMID: 30637135 BACKGROUND

• 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.

  PMID: 25086554 BACKGROUND

Biospecimen

Retention: SAMPLES WITHOUT DNA

MRI images

Study Officials

• Bugra Ince, MD

  Bezmialem Vakif University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 15, 2020
• First Posted: January 27, 2020
• Study Start: January 15, 2020
• Primary Completion: March 1, 2022
• Study Completion: April 1, 2022
• Last Updated: July 21, 2022

Record last verified: 2022-07

Locations

TU (1)