Clinical trial NCT04239638: Analysis of Cervical Spinal MRI With Deep Learning
Official title: Analysis of Cervical Spinal MRI With Deep Learning
Study type: OBSERVATIONAL
Overall status: WITHDRAWN
Conditions: Cervical Disc Disease
Cervical Spine Disease
Lead sponsor: Bezmialem Vakif University
Sponsor class: OTHER
First posted: 2020-01-27
Enrollment: 0
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with neck pain between 18-75 years

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.

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

Primary outcomes:
measure: Accuracy rate of the model as assessed by cross validation of the data set
timeFrame: Through study completion, an average of 1,5 years
measure: Reliability of the model as assessed by comparing the reports of the model and radiologist.
timeFrame: Through study completion, an average of 1,5 years
Secondary outcomes:
Other outcomes:
Locations:
city: Istanbul
country: Turkey
facility: Bezmialem Vakif University Hospital
Study officials:
affiliation: Bezmialem Vakif University
name: Bugra Ince, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
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):1656. doi: 10.3390/app8091656. Epub 2018 Sep 14.
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.