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Study identifier: NCT06061822 Synced from ClinicalTrials.gov · July 29, 2026
● Recruiting

Artificial Intelligence Delivered Cardiac Magnetic Resonance - Prospective Validation

Condition: Cardiovascular Diseases · Healthy Volunteers  ·  Sponsor: Imperial College London

PhaseNA
Planned participants150
Who can joinAll sexes, 18 Years to no upper limit
Healthy volunteersYes

About this study

Cardiac MRI (CMR) scanning allows doctors to create detailed images of the heart. However, the need for experienced cardiac radiographers to perform each scan can make CMR's delivery difficult, and some patients in the UK wait more than half a year for a scan. These radiographers must take pictures of different part of the heart, termed "views", each of which must be precisely positioned. The investigators believe they can revolutionise CMR, by using artificial intelligence to automatically position the views so radiographers can focus on more difficult tasks. The investigators have used a retrospective database of pseudonymised (anonymised and linked) CMR scans at our hospital to create these artificial intelligence (AI) algorithms, and they have validated them retrospectively on previous studies. The investigators now wish to test the algorithms prospectively. In this study, the investigators will recruit patients undergoing clinical CMR scans. In addition to the routine images acquired by expert radiographers, the investigators will require a duplicate set of images, positioned and planned by the AI algorithms. The investigators will then compare, within each patient, the AI-planned and expert-radiographer-planned scanning in terms of both speed and image quality.

This description comes directly from the study's public registry record.

Talk to the study team

James P Howard, MB BChir PhD  ·  +44 207 594 5735  ·  james.howard1@imperial.ac.uk

Always discuss trial participation with your own doctor first.

Locations (1)

Imperial College Healthcare NHS TrustLondon, United KingdomRecruiting

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Source record: clinicaltrials.gov/study/NCT06061822