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AI-enhanced LF-MRI in Epilepsy (ALF-ME)

21. Juli 2026 aktualisiert von: University College, London

Evaluation of Artificial Intelligence-enhanced Low Field

The global burden of epilepsy is high affecting over 50 million people worldwide. Majority live in low- and middleincome countries (LMICs) where access to diagnosis and treatment is limited. Accurate diagnosis of epilepsy and identification of the underlying cause through brain imaging is key to providing appropriate treatment. Magnetic resonance imaging (MRI) is the recommended modality of choice for brain imaging. However, in many LMICs it is scarce, and the cost of maintenance is unattainable. This study aims to explore the usefulness of a lower cost, more portable MRI machine for epilepsy diagnosis. It will be a proof-of-concept study evaluating the utility of low magnetic field MRI (LF-MRI) in epilepsy diagnosis. It will include 30 adults with epilepsy who have undergone a high field MRI (HF-MRI) brain scan as part of their routine clinical care under the University College London (UCL) Hospitals (UCLH), within twelve months of recruitment. Participants will be consecutively recruited and offered a LF-MRI brain scan on the Swoop MR Imaging System (Hyperfine) at the Birbeck-UCL Centre for Neuroimaging (BUCNI). Image post-processing will be performed using the open access machine learning program, LF-SynthSR, to enhance the image quality and allow for quantitative image analysis. Anonymised HF- and LF-MRI scans will be independently reported using a structured reporting template by two neuroradiologists. A perception survey will be administered to all participants to assess their tolerability of the LF-MRI. This study will serve as a foundation for future studies in this field and in areas where such innovations are most needed.

Studienübersicht

Status

Aktiv, nicht rekrutierend

Bedingungen

Intervention / Behandlung

Detaillierte Beschreibung

The aim of this study is to determine the utility of AI-enhanced Low Field (AI-LF) MRI in the identification of focal brain lesions in adults with epilepsy. This is a proof-of-concept study evaluating the utility of AI-LF-MRI in aiding identification of potentially epileptogenic brain lesions, by comparing neuroradiologists' lesion detection on this modality versus standard HF-MRI. It will include ~30 adults with epilepsy who have undergone a high field (1.5T or 3T) MRI (HF-MRI) brain scan as part of their routine clinical care at University College London (UCL) Hospitals (UCLH), within the previous twelve months. Participants will be consecutively recruited and offered a LF-MRI brain scan on the 0.064T Swoop MR Imaging System (Hyperfine®) at the Birkbeck-UCL Centre for Neuroimaging (BUCNI). Image post-processing will be performed using the open access machine learning program, LF-SynthSR, to enhance the image quality and allow for quantitative image analysis. Anonymised HF- and LF-MRI scans will be independently reported using a structured reporting template by two neuroradiologists. A perception survey will be administered to all participants to assess their tolerability of the LF-MRI. Each participant will only have a single encounter with the study on the day they get their LF-MRI scan. There is no follow-up required with this study.

Inclusion criteria:

  1. Adults aged between >18 years and <70 years
  2. Diagnosis of epilepsy and attending a UCLH-affiliated outpatient epilepsy clinic
  3. Undergone a 1.5T or 3T MRI Brain scan within 12 months of recruitment as part of standard clinical care.
  4. Can tolerate MRI scanning without sedation. Exclusion criteria

1. Cognitive impairment that precludes ability to consent or assent to the study 2. Inability to lie flat for the duration of the scan 3. Body habitus incompatible with LF-MRI device 4. Presence of MRI contraindications as stipulated by standard MRI operating procedures

We will recruit at least 30 patients, of these, 20 patients will have a visible lesion on the HF-MRI scan (reference standard). A sample size of 20 patients will be sufficient to demonstrate that the proportion of lesions similarly identified is at least 0.8, against a null hypothesis of 0.5, using a one-sample, one-sided exact binomial test with a 5% significance level and a power of 80%. The rest will not have a visible potentially epileptogenic lesion on the HF-MRI scan.

Two consultant neuroradiologists will separately and independently report AI-LF- then HF-MRI scans according to set criteria, including, MRI scanner field strength, presence of lesion, lesion location and artefacts. They will be provided with basic participant clinical details such as age, seizure type and. EEG findings, but blinded to diagnosis. Rates of lesion detection and false positives in the AI-LF-MRI scans will be compared with the reference standard of a recent clinical HF-MRI and reported as proportions. Sensitivity and specificity analyses will be performed for the qualitative analysis of AI-LF-MRI. Intraclass coefficients and Bland Altman analyses will be used to compare quantitative inter-rater and inter-method agreement, respectively. Descriptive statistics such as medians (IQR) and proportions will be used to describe participants' demographic information and survey responses.

Studientyp

Interventionell

Einschreibung (Tatsächlich)

39

Phase

  • Unzutreffend

Kontakte und Standorte

Dieser Abschnitt enthält die Kontaktdaten derjenigen, die die Studie durchführen, und Informationen darüber, wo diese Studie durchgeführt wird.

Studienorte

Teilnahmekriterien

Forscher suchen nach Personen, die einer bestimmten Beschreibung entsprechen, die als Auswahlkriterien bezeichnet werden. Einige Beispiele für diese Kriterien sind der allgemeine Gesundheitszustand einer Person oder frühere Behandlungen.

Zulassungskriterien

Studienberechtigtes Alter

  • Erwachsene
  • Älterer Erwachsener

Akzeptiert gesunde Freiwillige

Nein

Beschreibung

Inclusion Criteria:

  1. Adults aged between >17 years and <70 years
  2. Diagnosis of epilepsy and attending at UCLH-affiliated outpatient epilepsy clinic
  3. Undergone a 1.5T or 3T MRI Brain scan within 12 months of recruitment as part of standard clinical care
  4. Can tolerate MRI scanning without sedation.

Exclusion Criteria:

  1. Cognitive impairment that precludes ability to consent or assent to the study
  2. Inability to lie flat
  3. Body habitus incompatible with LF-MRI device
  4. Presence of MRI contraindications as stipulated by standard MRI operating procedures

Studienplan

Dieser Abschnitt enthält Einzelheiten zum Studienplan, einschließlich des Studiendesigns und der Messung der Studieninhalte.

Wie ist die Studie aufgebaut?

Designdetails

  • Hauptzweck: Diagnose
  • Zuteilung: N / A
  • Interventionsmodell: Einzelgruppenzuweisung
  • Maskierung: Keine (Offenes Etikett)

Waffen und Interventionen

Teilnehmergruppe / Arm
Intervention / Behandlung
Experimental: Low-field MRI Scan
MRI Brain scan on the Hyperfine 0.064T Swoop System
Low magnetic field MRI of the brain
Andere Namen:
  • Low-field

Was misst die Studie?

Primäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Sensitivity of AI-LF-MRI for lesion detection, measured as the proportion of lesions identified on HF-MRI that are also detected on AI-LF-MRI by blinded neuroradiologist review.
Zeitfenster: Baseline assessment (within the study imaging visit). Participant will only undergo a low-field MRI scan which will be compared to a reference high-field MRI scan done within 12 months of enrolment.
The rate of correctly identified abnormalities on the AI-enhanced low-field MRI when compared to standard of care high-field MRI. There is no pre-specified "good" rate assigned for this pilot study.
Baseline assessment (within the study imaging visit). Participant will only undergo a low-field MRI scan which will be compared to a reference high-field MRI scan done within 12 months of enrolment.

Sekundäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Proportion of scans with clinically significant artefacts, determined by blinded neuroradiologist assessment, on AI-LF-MRI compared with HF-MRI
Zeitfenster: Baseline assessment (within the study imaging visit)
Rate of reported artefacts, including movement, blurring and signal inhomogeneity, on AI-LF-MRI compared to HF-MRI
Baseline assessment (within the study imaging visit)
Inter-rater agreement among blinded neuroradiologists for qualitative lesion detection and classification on AI-LF-MRI and HF-MRI, measured using Cohen's kappa for pairwise agreement between raters and Fleiss' kappa for agreement across all raters
Zeitfenster: Baseline assessment (within the study imaging visit)
Compare agreement of lesions identified among radiologists reading the scans
Baseline assessment (within the study imaging visit)
Inter-method agreement between AI-LF-MRI and HF-MRI for quantitative imaging measures, assessed using Bland-Altman analysis.
Zeitfenster: Baseline assessment (within the study imaging visit)
Comparative analysis of hippocampal volume segmentation of the SuperSynth (AI tool) of low-field vs high-field MRI
Baseline assessment (within the study imaging visit)
Acceptability of the LF-MRI scanning process, assessed using participant responses to a custom acceptability questionnaire
Zeitfenster: Baseline assessment (within the study imaging visit)
Participants answer a custom-made questionnaire immediately after their low-field MRI scan on the Hyperfine. The questionnaire has closed-ended questions about their experience, with a few opportunities for open comment.
Baseline assessment (within the study imaging visit)

Mitarbeiter und Ermittler

Hier finden Sie Personen und Organisationen, die an dieser Studie beteiligt sind.

Ermittler

  • Hauptermittler: John S Duncan, DM, University College, London

Publikationen und hilfreiche Links

Die Bereitstellung dieser Publikationen erfolgt freiwillig durch die für die Eingabe von Informationen über die Studie verantwortliche Person. Diese können sich auf alles beziehen, was mit dem Studium zu tun hat.

Studienaufzeichnungsdaten

Diese Daten verfolgen den Fortschritt der Übermittlung von Studienaufzeichnungen und zusammenfassenden Ergebnissen an ClinicalTrials.gov. Studienaufzeichnungen und gemeldete Ergebnisse werden von der National Library of Medicine (NLM) überprüft, um sicherzustellen, dass sie bestimmten Qualitätskontrollstandards entsprechen, bevor sie auf der öffentlichen Website veröffentlicht werden.

Haupttermine studieren

Studienbeginn (Tatsächlich)

18. September 2025

Primärer Abschluss (Tatsächlich)

20. Februar 2026

Studienabschluss (Geschätzt)

31. August 2026

Studienanmeldedaten

Zuerst eingereicht

7. Juli 2026

Zuerst eingereicht, das die QC-Kriterien erfüllt hat

15. Juli 2026

Zuerst gepostet (Tatsächlich)

21. Juli 2026

Studienaufzeichnungsaktualisierungen

Letztes Update gepostet (Tatsächlich)

23. Juli 2026

Letztes eingereichtes Update, das die QC-Kriterien erfüllt

21. Juli 2026

Zuletzt verifiziert

1. Juli 2026

Mehr Informationen

Begriffe im Zusammenhang mit dieser Studie

Andere Studien-ID-Nummern

  • 352304
  • 179646 (Andere Zuschuss-/Finanzierungsnummer: University College London)
  • 25/NE/0149 (Andere Kennung: NHS Health Research Authority Reference number)

Plan für individuelle Teilnehmerdaten (IPD)

Planen Sie, individuelle Teilnehmerdaten (IPD) zu teilen?

UNENTSCHIEDEN

Beschreibung des IPD-Plans

Anonymised IPD may be shared with researchers within our institution (UCL). However, a plan has not yet been made for sharing the data more widely.

Arzneimittel- und Geräteinformationen, Studienunterlagen

Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt

Nein

Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt

Ja

Produkt, das in den USA hergestellt und aus den USA exportiert wird

Ja

Diese Informationen wurden ohne Änderungen direkt von der Website clinicaltrials.gov abgerufen. Wenn Sie Ihre Studiendaten ändern, entfernen oder aktualisieren möchten, wenden Sie sich bitte an register@clinicaltrials.gov. Sobald eine Änderung auf clinicaltrials.gov implementiert wird, wird diese automatisch auch auf unserer Website aktualisiert .

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