Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1200}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2021-11-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-07', 'completionDateStruct': {'date': '2022-08-15', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2022-07-21', 'studyFirstSubmitDate': '2022-07-21', 'studyFirstSubmitQcDate': '2022-07-21', 'lastUpdatePostDateStruct': {'date': '2022-07-25', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-07-25', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-07-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Objective evaluation', 'timeFrame': '30 minutes', 'description': 'using SSIM\\\\MAE index evaluation'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Arterial Aneurysm'], 'conditions': ['Arterial Aneurysm']}, 'descriptionModule': {'briefSummary': 'Computational imaging research based on deep learning', 'detailedDescription': 'Based on the current technical challenges, subject development and upgrade of knowledge, to avoid the occurrence of adverse medical accidents, simplify the diagnostic process, artificial intelligence has become the alternative method of choice, by constructing training deep learning model, the CTA as model inputs aneurysm detection and diagnosis to improve diagnosis effectiveness, promote the development of medical technology'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '80 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'varity of people', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Age: 18-80 years.\n2. CT paired imaging data of vessels, including layer-to-layer plain CT and enhanced CT. 2 Time range: January 2010 to December 2021.\n3. Scanning sites: CT and CTA of head, neck, chest, abdomen or iliac.\n\nExclusion Criteria:\n\n1. Unpaired CT image .\n2. Severe artifact CT image.\n3. enhancement CT failure image (failure to capture arterial phase or poor arterial development, insufficient flow of contrast agent, etc.)'}, 'identificationModule': {'nctId': 'NCT05471869', 'briefTitle': 'Computational Imaging Research Based on Deep Learning', 'organization': {'class': 'OTHER', 'fullName': 'Chinese PLA General Hospital'}, 'officialTitle': 'Computational Imaging Using Pixel-level Graph Adversarial Learning', 'orgStudyIdInfo': {'id': 'Synthesis imaging-ChinaPLAGH'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Aneurysm diagnosis', 'type': 'DIAGNOSTIC_TEST', 'description': 'Intelligent detection and diagnosis of aneurysm diagnosis by CTA'}]}, 'contactsLocationsModule': {'locations': [{'zip': '100853', 'city': 'Beijing', 'country': 'China', 'facility': 'Chinese PLA General Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Chinese PLA General Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'professor', 'investigatorFullName': 'Xin Lou', 'investigatorAffiliation': 'Chinese PLA General Hospital'}}}}