AI-powered Portable MRI Abnormality Detection (APPMAD)

January 27, 2025 updated by: King's College Hospital NHS Trust

This study aims to test a new AI-powered portable MRI scanner that can quickly identify whether a brain scan is normal or abnormal. Currently, standard MRI scans are expensive and have long waiting times. Our goal is to see if a smaller, cheaper, and more accessible MRI scanner-combined with artificial intelligence (AI)-can help doctors identify abnormalities faster and improve patient care.

We will invite patients from King's College Hospital (KCH) who are already having a standard MRI scan. They will be asked to have an extra scan using the portable MRI, which takes about 60 minutes. The AI tool will then analyse these scans and compare its results to those of expert radiologists.

By the end of the study, we hope to prove whether portable MRI with AI can be used in hospitals and GP clinics, making brain scans more accessible, reducing wait times, and helping doctors prioritise urgent cases.

This study is funded by the Medical Research Council (MRC) and has been approved by UK research ethics committees.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Estimated)

400

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

Adults ≥18 years old. Undergoing standard brain MRI including T2-weighted sequences.

Exclusion Criteria:

Contraindications to MRI (e.g. pacemaker, pregnancy). Poor quality MRI scans without a neuroradiology report.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Screening
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Portable, ultra-low-field MRI scanner
Patients undergoing a standard brain MRI scan will be invited to have an additional portable MRI scan within 30 days of their clinical scan.
Device: Portable, ultra-low-field MRI scanner

This study evaluates a portable, ultra-low-field MRI scanner (the Hyperfine Swoop) combined with artificial intelligence (AI) to detect brain abnormalities.

Patients undergoing a standard brain MRI scan will be invited to have an additional portable MRI scan within 30 days of their clinical scan. The portable MRI scan will take approximately 60 minutes, using multiple imaging sequences, including T2-weighted scans.

The AI system will then analyse the portable MRI images and categorise them as "normal" or "abnormal". The results will be compared with expert neuroradiologist reports from standard MRI scans to validate accuracy.

This intervention aims to assess whether portable MRI with AI can provide a low-cost, accessible alternative to standard MRI, potentially improving triage and reducing waiting times for patients requiring urgent brain imaging.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of AI toll for triaging scans as "normal or "abnormal"
Time Frame: 36 months
Ai Triage accuracy compared with consultant neuroradiologists assessment.
36 months

Secondary Outcome Measures

Outcome Measure
Time Frame
Generalisability of AI tool (evaluated on external dataset).
Time Frame: 36 months
36 months
Patient acceptability of portable MRI (survey/interviews)
Time Frame: 36 months
36 months
Feasibility of integrating portable MRI in clinical pathways.
Time Frame: 36 months
36 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

King's College Hospital NHS Trust

Collaborators

King's College London

Investigators

  • Principal Investigator: Thomas Booth, Dr, King's College London & King's College Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

February 1, 2025

Primary Completion (Estimated)

October 1, 2027

Study Completion (Estimated)

October 1, 2027

Study Registration Dates

First Submitted

January 27, 2025

First Submitted That Met QC Criteria

January 27, 2025

First Posted (Actual)

March 25, 2025

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

January 27, 2025

Last Verified

January 1, 2025

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • IRAS 347453

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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