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

NCT ID: NCT03920995

Last Updated: 2024-03-06

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 1000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-04-01
• Study Completion Date: 2023-10-31

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.

Conditions

Multiple Sclerosis

Study Design

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

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

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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 85 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Celgene

INDUSTRY

Sponsor Role: collaborator

University at Buffalo

OTHER

Sponsor Role: lead

Responsible Party

Robert Zivadinov, MD, PhD

Director and Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Robert Zivadinov

Role: PRINCIPAL_INVESTIGATOR

University at Buffalo

Locations

University at Buffalo

Buffalo, New York, United States

Countries

United States

Other Identifiers

N/A-NI-MS-PI-13632

Identifier Type: -

Identifier Source: org_study_id