Ignite Creation Date:
2025-12-25 @ 12:45 AM
Ignite Modification Date:
2025-12-26 @ 1:30 PM
Study NCT ID:
NCT06769867
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-03-18
First Post:
2025-01-02
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
(Withdrawal) AI-Based Low-Dose 3D-DSA Reconstruction
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D002561', 'term': 'Cerebrovascular Disorders'}], 'ancestors': [{'id': 'D001927', 'term': 'Brain Diseases'}, {'id': 'D002493', 'term': 'Central Nervous System Diseases'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D014652', 'term': 'Vascular Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NON_RANDOMIZED', 'maskingInfo': {'masking': 'TRIPLE', 'whoMasked': ['PARTICIPANT', 'INVESTIGATOR', 'OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'CROSSOVER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 134}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-04-15', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-03', 'completionDateStruct': {'date': '2025-05-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-03-13', 'studyFirstSubmitDate': '2025-01-02', 'studyFirstSubmitQcDate': '2025-01-07', 'lastUpdatePostDateStruct': {'date': '2025-03-18', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-01-10', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-05-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Comparison of image quality using two scanning protocols (classic 3D-DSA and PS-3D-DSA)', 'timeFrame': 'No more than 1 month', 'description': "Algorithmic performance for image quality and interventional radiologists' scoring of image. Specifically, multiple radiologists will provide subjective ratings of image quality, with the best quality rated as 5 and the worst as 1.\n\nImage Quality (5 points):\n\n1. point: The smoothness of the video is very poor, with noticeable stuttering in the animation, making it nearly impossible to provide a smooth viewing experience.\n2. points: The smoothness of the video is poor, with a delayed animation, resulting in a less-than-ideal viewing experience.\n3. points: The smoothness of the video is average, the animation is relatively smooth, offering a satisfactory viewing experience.\n4. points: The smoothness of the video is high, with smooth and natural animation, providing an excellent viewing experience.\n5. points: The smoothness of the video is extremely high, with exceptionally smooth animation, providing an outstanding viewing experience."}], 'primaryOutcomes': [{'measure': 'The radiation dose received by patients during interventional procedures when using two scanning protocols (classic 3D-DSA and PS-3D-DSA)', 'timeFrame': 'No more than 6 hours', 'description': "Using the built-in radiation monitoring function of interventional surgical equipment (DSA system), the radiation dose (AK，air kerma) received by the patient during the procedure is directly measured and recorded. The collected radiation dose data is documented in the patient's medical records and stored in the research database for subsequent analysis and comparative studies."}], 'secondaryOutcomes': [{'measure': 'The image diagnostic capabilities using two scanning protocols (classic 3D-DSA and PS-3D-DSA)', 'timeFrame': 'No more than 1 month', 'description': 'The secondary outcomes focus on evaluating the performance of interventional radiologists using either PS-3D-DSA or classic 3D-DSA for diagnostic tasks, with accuracy as the primary measure. Specifically, the diagnostic results from multiple radiologists will be compared against the gold standard to determine the level of agreement and diagnostic accuracy. This comparison will involve calculating sensitivity, specificity, and overall accuracy. Furthermore, receiver operating characteristic (ROC) curves will be plotted to assess the diagnostic performance and to visually represent the trade-off between sensitivity and specificity for each method.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Cerebrovascular', 'AI', '3D-DSA'], 'conditions': ['Cerebrovascular Disease']}, 'referencesModule': {'references': [{'pmid': '26553142', 'type': 'BACKGROUND', 'citation': 'van Asch CJ, Velthuis BK, Rinkel GJ, Algra A, de Kort GA, Witkamp TD, de Ridder JC, van Nieuwenhuizen KM, de Leeuw FE, Schonewille WJ, de Kort PL, Dippel DW, Raaymakers TW, Hofmeijer J, Wermer MJ, Kerkhoff H, Jellema K, Bronner IM, Remmers MJ, Bienfait HP, Witjes RJ, Greving JP, Klijn CJ; DIAGRAM Investigators. Diagnostic yield and accuracy of CT angiography, MR angiography, and digital subtraction angiography for detection of macrovascular causes of intracerebral haemorrhage: prospective, multicentre cohort study. BMJ. 2015 Nov 9;351:h5762. doi: 10.1136/bmj.h5762.'}, {'pmid': '37556901', 'type': 'BACKGROUND', 'citation': 'Irfan M, Malik KM, Ahmad J, Malik G. StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm. Comput Med Imaging Graph. 2023 Sep;108:102271. doi: 10.1016/j.compmedimag.2023.102271. Epub 2023 Jul 22.'}]}, 'descriptionModule': {'briefSummary': 'If the participants agree to participate in this study, the participants will undergo two scans (classic 3D-DSA and PS-3D-DSA assisted scan) to compare the imaging effects of both. After the procedure, the investigators will record the radiation exposure and collect DSA images.', 'detailedDescription': 'Although several previous studies have used deep learning methods to reduce 3D-DSA radiation dose, no prospective clinical trial had yet validated the practical application of these models. Herein, the investigators introduce a patient-specific generative AI-based low-dose cerebrovascular 3D-DSA image reconstruction method (PS-3D-DSA) to reconstruct 3D-DSA images from ultra-sparse 2D projection views and a prospective cohort is used to validate its efficacy in clinical practice.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': "Inclusion Criteria:\n\n1. Age ≥18 years.\n2. Requires 3D-DSA-guided interventional diagnosis or treatment (e.g., cerebral angiography, cerebral artery chemoembolization) and meets operational indications.\n3. Can understand the study's purpose, procedures, potential risks, and benefits, and voluntarily signs a written informed consent form.\n\nExclusion Criteria:\n\n1. Severe heart or lung disease, such as heart failure or chronic obstructive pulmonary disease (COPD).\n2. History of high-dose radiation exams or treatments.\n3. Known allergies or severe adverse reactions to iodine contrast agents or other relevant medications.\n4. Pregnant or breastfeeding women.\n5. Severe comorbidities or chronic diseases (e.g., severe diabetes, renal insufficiency).\n6. Severe mental illness or cognitive impairment preventing understanding of the study procedures or providing informed consent."}, 'identificationModule': {'nctId': 'NCT06769867', 'briefTitle': '(Withdrawal) AI-Based Low-Dose 3D-DSA Reconstruction', 'organization': {'class': 'OTHER', 'fullName': 'Union Hospital, Tongji Medical College, Huazhong University of Science and Technology'}, 'officialTitle': '(Withdrawal) Validation of a Patient-Specific Generative AI-Based Low-Dose Cerebrovascular 3D-DSA Image Reconstruction Method: a Stepwise, Multicenter, Randomized Crossover Trial', 'orgStudyIdInfo': {'id': 'Patient-Specific Generative AI'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'PS-3D-DSA', 'description': 'undergo a PS-3D-DSA scan', 'interventionNames': ['Radiation: PS-3D-DSA']}, {'type': 'SHAM_COMPARATOR', 'label': 'classic 3D-DSA', 'description': 'undergo a classic 3D-DSA scan', 'interventionNames': ['Radiation: classic 3D-DSA']}], 'interventions': [{'name': 'PS-3D-DSA', 'type': 'RADIATION', 'description': 'undergo a PS-3D-DSA scan', 'armGroupLabels': ['PS-3D-DSA']}, {'name': 'classic 3D-DSA', 'type': 'RADIATION', 'description': 'undergo a classic 3D-DSA scan', 'armGroupLabels': ['classic 3D-DSA']}]}, 'contactsLocationsModule': {'locations': [{'zip': '430022', 'city': 'Wuhan', 'state': 'Hubei', 'country': 'China', 'facility': 'Wuhan Union Hospital', 'geoPoint': {'lat': 30.58333, 'lon': 114.26667}}], 'centralContacts': [{'name': 'Huangxuan Zhao, PhD', 'role': 'CONTACT', 'email': 'zhao_huangxuan@sina.com', 'phone': '+86 18627162379'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Union Hospital, Tongji Medical College, Huazhong University of Science and Technology', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'MD', 'investigatorFullName': 'Yaowei Bai', 'investigatorAffiliation': 'Huazhong University of Science and Technology'}}}}