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Multi-center Database Registry to Study Thalamus Changes Using AI in MS

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ClinicalTrials.gov Identifier: NCT03920995
Recruitment Status : Active, not recruiting
First Posted : April 19, 2019
Last Update Posted : January 6, 2021
Sponsor:
Collaborator:
Celgene
Information provided by (Responsible Party):
Robert Zivadinov, MD, PhD, University at Buffalo

Tracking Information
First Submitted Date March 12, 2019
First Posted Date April 19, 2019
Last Update Posted Date January 6, 2021
Actual Study Start Date May 13, 2019
Estimated Primary Completion Date September 30, 2021   (Final data collection date for primary outcome measure)
Current Primary Outcome Measures
 (submitted: April 18, 2019)
Multi-center registry of MRI scans [ Time Frame: 2 years ]
Measuring the ability of DeepGRAI to measure thalamus volume as a predictor of clinical outcomes for patients with multiple sclerosis
Original Primary Outcome Measures
 (submitted: April 15, 2019)
Multi-center registry of MRI scans [ Time Frame: 2 years ]
Measuring the ability of DeepGRAI's ability to measure thalamus volume can predict clinical outcomes for patients with multiple sclerosis
Change History
Current Secondary Outcome Measures Not Provided
Original Secondary Outcome Measures Not Provided
Current Other Pre-specified Outcome Measures Not Provided
Original Other Pre-specified Outcome Measures Not Provided
 
Descriptive Information
Brief Title Multi-center Database Registry to Study Thalamus Changes Using AI in MS
Official Title Creation of a Multi-center Database Registry to Study Real World Thalamus Volume Changes by Use of Artificial Intelligence in Patients With Multiple Sclerosis (MS)
Brief Summary In this study the Investigator's propose to validate a newly developed approach, DeepGRAI (Deep Gray Rating via Artificial Intelligence), to simplify the calculation of thalamic atrophy in a clinical routine and allow academic and community neurologists to plan, perform, and publish novel and influential clinical research using data from clinical routine, by employing deep machine learning (DML) pattern recognition (PR) information through use of artificial intelligence (AI).
Detailed Description This is a multicenter, observational, retrospective, cross-sectional and longitudinal population study of brain volume changes in MS patients. The retrospective electronic medical record (EMR) and brain MRI image data will be collected at participating MS centers and de-identified data will be integrated into a central research database. All the data to be integrated into the database has already been collected by physicians at the centers as part of their routine clinical practice and is thus non-interventional and retrospective in nature. This new approach will be compared to existing approaches of brain volume measurement that are currently widely available. This breakthrough approach would lead to potentially abandoning classis measurement of the specific brain volume structures and would be applicable in real-time in clinical routine.
Study Type Observational
Study Design Observational Model: Case-Only
Time Perspective: Retrospective
Target Follow-Up Duration Not Provided
Biospecimen Not Provided
Sampling Method Non-Probability Sample
Study Population Individuals with Multiple sclerosis who have 2 MRI scans
Condition Multiple Sclerosis
Intervention Not Provided
Study Groups/Cohorts Not Provided
Publications * Not Provided

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruitment Information
Recruitment Status Active, not recruiting
Actual Enrollment
 (submitted: April 15, 2019)
1000
Original Estimated Enrollment Same as current
Estimated Study Completion Date December 31, 2021
Estimated Primary Completion Date September 30, 2021   (Final data collection date for primary outcome measure)
Eligibility Criteria

Inclusion Criteria:

  1. Patient diagnosed with relapsing-remitting (RR) MS
  2. Access to raw MRI index scan images that meet all of the below criteria

    1. MRI scan image acquired at index
    2. The scan was performed on 1.5T or 3T scanners
    3. The scan must have a T2-FLAIR sequence
  3. Access to raw MRI post-index scan images that meet all of the below listed criteria

    1. MRI scan image acquired at post-index
    2. The scan was performed on 1.5T or 3T scanners
    3. The scan must have a T2-FLAIR sequence
  4. Age 18-85 at index
  5. Fulfilling the MRI scan and clinical data requirements outlined in Table 2
  6. None of the exclusion criteria

Exclusion Criteria:

  1. Have received an investigational drug or experimental procedure during the study period
  2. Women who were pregnant, or lactating at index or during the post-index period
  3. Patients who had a relapse 30 days prior to the selected MRI scan date
  4. Patients who received steroid treatment 30 days prior to the selected MRI scan date
  5. Presence of other neurologic diseases affecting CNS
Sex/Gender
Sexes Eligible for Study: All
Ages 18 Years to 85 Years   (Adult, Older Adult)
Accepts Healthy Volunteers No
Contacts Contact information is only displayed when the study is recruiting subjects
Listed Location Countries United States
Removed Location Countries  
 
Administrative Information
NCT Number NCT03920995
Other Study ID Numbers N/A-NI-MS-PI-13632
Has Data Monitoring Committee No
U.S. FDA-regulated Product
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
IPD Sharing Statement
Plan to Share IPD: No
Responsible Party Robert Zivadinov, MD, PhD, University at Buffalo
Study Sponsor University at Buffalo
Collaborators Celgene
Investigators
Principal Investigator: Robert Zivadinov University at Buffalo
PRS Account University at Buffalo
Verification Date January 2021