AID-MR: Artificial Intelligence Delivered Cardiac Magnetic Resonance - Prospective Validation

Sponsor
Imperial College London (Other)
Overall Status
Not yet recruiting
CT.gov ID
NCT06061822
Collaborator
British Heart Foundation (Other), Medical Research Council (Other), Rosetrees Trust (Other)
150
2
48

Study Details

Study Description

Brief 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.

Condition or Disease Intervention/Treatment Phase
  • Diagnostic Test: AI-assisted cardiac magnetic resonance imaging
N/A

Study Design

Study Type:
Interventional
Anticipated Enrollment :
150 participants
Allocation:
Randomized
Intervention Model:
Crossover Assignment
Masking:
Double (Participant, Outcomes Assessor)
Primary Purpose:
Diagnostic
Official Title:
Artificial Intelligence Delivered Cardiac Magnetic Resonance - Prospective Validation
Anticipated Study Start Date :
Dec 1, 2023
Anticipated Primary Completion Date :
Dec 1, 2027
Anticipated Study Completion Date :
Dec 1, 2027

Arms and Interventions

Arm Intervention/Treatment
Experimental: AI-planned images acquired

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.

Active Comparator: Radiographer-planned images acquired

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.

Outcome Measures

Primary Outcome Measures

  1. Time taken to acquire images [During the MRI scan]

    The time taken in seconds from the beginning of the planning process, until the last planned images has been fully acquired.

  2. Image quality [During the MRI scan]

    Quality of acquired images (AI-planning versus radiographer planning) assessed by level 3 cardiac MRI accredited doctors, by asking them to choose whether (a) the AI-acquired image is of higher diagnostic quality, (b) the radiographer-acquired image is of higher diagnostic quality, or (c) the AI- and radiographer-acquired images are of identical diagnostic quality.

Eligibility Criteria

Criteria

Ages Eligible for Study:
18 Years and Older
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
Yes
Inclusion Criteria:
  • Adult (aged at least 18 years)
Exclusion Criteria:
  • Children (patients below age 18).

  • Pregnant patients.

Contacts and Locations

Locations

No locations specified.

Sponsors and Collaborators

  • Imperial College London
  • British Heart Foundation
  • Medical Research Council
  • Rosetrees Trust

Investigators

  • Principal Investigator: James P Howard, MB BChir PhD, Imperial College London

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.
Responsible Party:
Imperial College London
ClinicalTrials.gov Identifier:
NCT06061822
Other Study ID Numbers:
  • 23HH8238
First Posted:
Sep 29, 2023
Last Update Posted:
Sep 29, 2023
Last Verified:
Sep 1, 2023
Individual Participant Data (IPD) Sharing Statement:
Undecided
Plan to Share IPD:
Undecided
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Additional relevant MeSH terms:

Study Results

No Results Posted as of Sep 29, 2023