Artificial Intelligence Delivered Cardiac Magnetic Resonance - Prospective Validation

Sponsor
Imperial College London
Study ID
NCT06061822
Status
Recruiting
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Conditions

  • Cardiovascular Diseases
  • Healthy Volunteers

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Accepted

Interventions

  • AI-assisted cardiac magnetic resonance imaging — DIAGNOSTIC_TEST
    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.

Study Details

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.

Key Dates

First listed
Sep 29, 2023
Start date
May 1, 2026
Status verified
May 2026
Primary completion
Dec 1, 2027
Completion
Dec 1, 2027

Study Design

Enrollment
150 participants (estimated)
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
DIAGNOSTIC

Arms

  • Experimental: AI-planned images acquired
  • Active Comparator: Radiographer-planned images acquired

Primary Outcome Measure

Time taken to acquire images [ Time Frame: During the MRI scan ]

Central Contacts

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