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Artificial Intelligence Delivered Cardiac Magnetic Resonance - Prospective Validation

NCT06061822 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 150

Last updated 2026-05-11

No results posted yet for this study

Summary

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.

Conditions

Interventions

DIAGNOSTIC_TEST

AI-assisted cardiac magnetic resonance imaging

An AI algorithm will be used to automatically position (plan) the scan planes used in a cardiac MRI scan. The resultant images will be compared with standard radiographer-positioned images.

Sponsors & Collaborators

  • British Heart Foundation

    collaborator OTHER

  • Medical Research Council

    collaborator OTHER_GOV

  • Rosetrees Trust

    collaborator OTHER

  • Imperial College London

    lead OTHER

Principal Investigators

  • James P Howard, MB BChir PhD · Imperial College London

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
DOUBLE
Model
CROSSOVER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-05-01
Primary Completion
2027-12-01
Completion
2027-12-01

Countries

  • United Kingdom

Study Locations

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Diseases
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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 NCT06061822 on ClinicalTrials.gov