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NCT Number: NCT06803420

AI-powered Portable MRI Abnormality Detection

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.

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Key information

Conditions

Head Injury Craniocerebral Trauma Nervous System Diseases Trauma, Nervous System Wounds and Injuries

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Portable, ultra-low-field MRI scanner

Device

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.

Primary outcomes

  1. Accuracy of AI toll for triaging scans as "normal or "abnormal"

    Time frame: 36 months

    Ai Triage accuracy compared with consultant neuroradiologists assessment.

Secondary outcomes

  1. Generalisability of AI tool (evaluated on external dataset).

    Time frame: 36 months

  2. Patient acceptability of portable MRI (survey/interviews)

    Time frame: 36 months

  3. Feasibility of integrating portable MRI in clinical pathways.

    Time frame: 36 months

Study contacts

Contact information is provided by the study sponsor or research team.

Frantisek Vasa, PhD

CONTACT

[email protected]

020 7848 9670

Giusi Manfredi, PhD

CONTACT

[email protected]

020 7848 9670

Sponsors and collaborators

Lead sponsor

King's College Hospital NHS Trust

Other

Collaborators

  • King's College London

Registry information

Acronym: APPMAD

Important dates

Study start
2025
Primary completion
2027
Study completion
2027
First posted
Jan 31, 2025
Registry last updated
Jan 31, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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