Head InjuryAPPMADNCT06803420
AI-powered Portable MRI Abnormality Detection
A study testing Portable, ultra-low-field MRI scanner in people with Head Injury, run by King's College Hospital NHS Trust.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Head Injury
- What is being tested?
- Portable, ultra-low-field MRI scanner
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 400 participants
- Where
- No sites listed yet
- Study dates
- 1 Feb 2025to Oct 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Adults ≥18 years old. Undergoing standard brain MRI including T2-weighted sequences.
Who cannot join
- Contraindications to MRI (e.g. pacemaker, pregnancy). Poor quality MRI scans without a neuroradiology report.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
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.
Read more
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.
- Conditions
- Head Injury
- Being tested
- Portable, ultra-low-field MRI scanner
- Medical subject headings
- Craniocerebral Trauma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Portable, ultra-low-field MRI scannerDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| Portable, ultra-low-field MRI scanner | Experimental | Patients undergoing a standard brain MRI scan will be invited to have an additional portable MRI scan within 30 days of their clinical scan. Receives: Device: Portable, ultra-low-field MRI scanner |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of AI toll for triaging scans as "normal or "abnormal"
Ai Triage accuracy compared with consultant neuroradiologists assessment.
Measured: 36 months
Secondary outcomes (3)
Generalisability of AI tool (evaluated on external dataset).
Measured: 36 months
Patient acceptability of portable MRI (survey/interviews)
Measured: 36 months
Feasibility of integrating portable MRI in clinical pathways.
Measured: 36 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Jan 2025
- Study start1 Feb 2025 (estimated)
- Primary completionOct 2027 (estimated)
- Study completionOct 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Head InjuryStudies of the same condition, open and under way.
- More trials run by King's College Hospital NHS TrustThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- King's College Hospital NHS Trust
- Sponsor type
- University, hospital or other
- Collaborators
- King's College London
- Responsible party
- Sponsor
- Investigators
- Thomas Booth, Dr, Principal investigator, King's College London & King's College Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06803420
- Sponsor's protocol ID
- IRAS 347453
- Acronym
- APPMAD
- Status last verified
- Jan 2025
- First submitted
- 27 Jan 2025
- Last updated
- 31 Jan 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 31 Jan 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-powered Portable MRI Abnormality Detection
- ClinicalTrials.gov ID
- NCT06803420
- Acronym / sponsor ID
- APPMAD / IRAS 347453
- Conditions
- Head Injury
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- King's College Hospital NHS TrustUniversity, hospital or other
- Collaborators
- King's College London
- Study start
- 1 Feb 2025 (estimated)
- Primary completion
- Oct 2027 (estimated)
- Study completion
- Oct 2027 (estimated)
- First posted
- 31 Jan 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Screening
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Portable, ultra-low-field MRI scanner | Experimental | Device: Portable, ultra-low-field MRI scannerPatients undergoing a standard brain MRI scan will be invited to have an additional portable MRI scan within 30 days of their clinical scan. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Adults ≥18 years old. Undergoing standard brain MRI including T2-weighted sequences.
Key exclusion criteria (1 listed)
- Contraindications to MRI (e.g. pacemaker, pregnancy). Poor quality MRI scans without a neuroradiology report.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Portable, ultra-low-field MRI scannerThis 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. | Device | Portable, ultra-low-field MRI scanner |
Primary outcome
Accuracy of AI toll for triaging scans as "normal or "abnormal"
Time frame: 36 months
Ai Triage accuracy compared with consultant neuroradiologists assessment.
3 secondary outcomes
Generalisability of AI tool (evaluated on external dataset).
Time frame: 36 months
Patient acceptability of portable MRI (survey/interviews)
Time frame: 36 months
Feasibility of integrating portable MRI in clinical pathways.
Time frame: 36 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Jan 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Thomas Booth, Dr principal investigator
King's College London & King's College Hospital
Questions about this trial
What is this clinical trial studying?
A study testing Portable, ultra-low-field MRI scanner in people with Head Injury, run by King's College Hospital NHS Trust.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 31 Jan 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Frantisek Vasa, PhD (020 7848 9670, Frantisek.Vasa@kcl.ac.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of AI toll for triaging scans as "normal or "abnormal" (time frame: 36 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Jan 2025.