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Artificial Intelligence-Assisted Magnetic Resonance Imaging for Quality, Efficiency and Equity in the National Health Service (NHS) Care of Multiple Sclerosis

NCT07111637 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 1336

Last updated 2025-08-13

No results posted yet for this study

Summary

Multiple Sclerosis (MS) is a long-term disease that affects over 150,000 people in the UK. Starting treatment early is important for managing Multiple Sclerosis (MS). It is also essential to monitor the treatment to see if it is working and to switch treatments if needed.

Magnetic resonance imaging (MRI) is the only accepted tool to monitor how well the treatment is working. Current evaluation of brain Magnetic resonance imaging (MRI) scans requires visual inspection, of which sensitivity is degraded by human, and technical factors, such as lack of time, fatigue of radiologists, and lack of standardization of image acquisition protocols across the National Health Service (NHS). MRI-readings can be significantly enhanced by artificial intelligence (AI)-assistive software. Evidence suggests the rate of new lesion detection to be 3 - 4 times higher when using assistive software compared to visual inspection of MRI scans.

In this study, an Artificial Intelligence (AI) software called "icobrain ms." developed by the company "icometrix" (Leuven, Belgium) is tested. This tool helps track MS by measuring changes in the brain using MRI scans. The AI can highlight problem areas and create reports that doctors can use to make better decisions about participants' treatment. The aim of the study is to prove that icobrain ms can be used to assist the neuro-radiologist with their visual assessment of MRI scans by a radiologist, and that it will help clinicians make more informed decisions about participants' current MS treatment.

Conditions

Interventions

DEVICE

AI software

icobrain ms is an Artificial Intelligence (AI)-assistive software which quantifies brain Magnetic Resonance Imaging (MRIs) and summarises clinically relevant findings for patients with Multiple Sclerosis in structured electronic radiological reports and annotated images.

Sponsors & Collaborators

  • icometrixLeuven

    collaborator INDUSTRY

  • Queen Mary University of London

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
18 Years
Max Age
99 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-06-14
Primary Completion
2026-04-14
Completion
2026-04-14

Countries

  • United Kingdom

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07111637 on ClinicalTrials.gov