Ignite Creation Date:
2025-12-25 @ 4:10 AM
Ignite Modification Date:
2026-01-09 @ 10:16 PM
Study NCT ID:
NCT06803420
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-01-31
First Post:
2025-01-27
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI-powered Portable MRI Abnormality Detection
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D006259', 'term': 'Craniocerebral Trauma'}], 'ancestors': [{'id': 'D020196', 'term': 'Trauma, Nervous System'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D014947', 'term': 'Wounds and Injuries'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'SCREENING', 'interventionModel': 'SINGLE_GROUP'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 400}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-02-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-01', 'completionDateStruct': {'date': '2027-10', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-01-27', 'studyFirstSubmitDate': '2025-01-27', 'studyFirstSubmitQcDate': '2025-01-27', 'lastUpdatePostDateStruct': {'date': '2025-01-31', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-01-31', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-10', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Accuracy of AI toll for triaging scans as "normal or "abnormal"', 'timeFrame': '36 months', 'description': 'Ai Triage accuracy compared with consultant neuroradiologists assessment.'}], 'secondaryOutcomes': [{'measure': 'Generalisability of AI tool (evaluated on external dataset).', 'timeFrame': '36 months'}, {'measure': 'Patient acceptability of portable MRI (survey/interviews)', 'timeFrame': '36 months'}, {'measure': 'Feasibility of integrating portable MRI in clinical pathways.', 'timeFrame': '36 months'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Head Injury']}, 'descriptionModule': {'briefSummary': "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.\n\nWe 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.\n\nBy 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.\n\nThis study is funded by the Medical Research Council (MRC) and has been approved by UK research ethics committees."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\nAdults ≥18 years old. Undergoing standard brain MRI including T2-weighted sequences.\n\nExclusion Criteria:\n\nContraindications to MRI (e.g. pacemaker, pregnancy). Poor quality MRI scans without a neuroradiology report.'}, 'identificationModule': {'nctId': 'NCT06803420', 'acronym': 'APPMAD', 'briefTitle': 'AI-powered Portable MRI Abnormality Detection', 'organization': {'class': 'OTHER', 'fullName': "King's College Hospital NHS Trust"}, 'officialTitle': 'AI-powered Portable MRI Abnormality Detection', 'orgStudyIdInfo': {'id': 'IRAS 347453'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Portable, ultra-low-field MRI scanner', 'description': 'Patients undergoing a standard brain MRI scan will be invited to have an additional portable MRI scan within 30 days of their clinical scan.', 'interventionNames': ['Device: Portable, ultra-low-field MRI scanner']}], 'interventions': [{'name': 'Portable, ultra-low-field MRI scanner', 'type': 'DEVICE', 'description': 'This study evaluates a portable, ultra-low-field MRI scanner (the Hyperfine Swoop) combined with artificial intelligence (AI) to detect brain abnormalities.\n\nPatients 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.\n\nThe 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.\n\nThis 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.', 'armGroupLabels': ['Portable, ultra-low-field MRI scanner']}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Frantisek Vasa, PhD', 'role': 'CONTACT', 'email': 'Frantisek.Vasa@kcl.ac.uk', 'phone': '020 7848 9670'}, {'name': 'Giusi Manfredi, PhD', 'role': 'CONTACT', 'email': 'giusi.manfredi@kcl.ac.uk', 'phone': '020 7848 9670'}], 'overallOfficials': [{'name': 'Thomas Booth, Dr', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': "King's College London & King's College Hospital"}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': "King's College Hospital NHS Trust", 'class': 'OTHER'}, 'collaborators': [{'name': "King's College London", 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}