Ignite Creation Date:
2025-12-25 @ 12:21 AM
Ignite Modification Date:
2025-12-25 @ 10:25 PM
Study NCT ID:
NCT07040358
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-09-18
First Post:
2025-06-19
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Rapid Abdominal Diagnosis With AI & Radiology
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2000000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2023-12-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-09', 'completionDateStruct': {'date': '2026-06', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-09-14', 'studyFirstSubmitDate': '2025-06-19', 'studyFirstSubmitQcDate': '2025-06-19', 'lastUpdatePostDateStruct': {'date': '2025-09-18', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-06-27', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Performance of AI Model for Lesion Detection on Abdominal Contrast-Enhanced CT', 'timeFrame': 'After internal and external validation datasets are processed (estimated 6-12 months)', 'description': 'The primary outcome is the overall performance of the AI model in detecting and characterizing lesions in abdominal organs using multiphase contrast-enhanced CT scans. Performance will be measured using area under the receiver operating characteristic curve (AUC), F1-score, sensitivity, and specificity, with expert radiologist consensus reports as the reference standard.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial Intelligence', 'abdominal diseases', 'contrast-enhanced CT', 'RADAR'], 'conditions': ['Abdominal Diseases']}, 'descriptionModule': {'briefSummary': 'This study aims to develop an AI-assisted diagnostic system for abdominal contrast-enhanced CT images using data from multiple inpatient centers. In collaboration with Alibaba DAMO Academy, the project will address key mathematical challenges limiting current automated image interpretation, including feature space alignment, hybrid reasoning, and multimodal report generation. The study includes the following components: (1) construction of a dual-modality foundation model to align abdominal CT features with corresponding radiology reports; (2) development of a model to standardize CT phase variation among patients; and (3) creation of an automated image interpretation and reporting system that integrates multi-source clinical data. The effectiveness of the system will be evaluated through a report quality assessment framework and clinical validation. This project aims to improve the accuracy and clinical applicability of automated abdominal disease interpretation and promote intelligent innovation in healthcare delivery.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'This study uses a retrospective multicenter cohort comprising approximately 2 million cases of multiphase contrast-enhanced abdominal CT scans. All included imaging data are paired with corresponding radiology reports. The dataset reflects real-world imaging scenarios of various abdominal diseases.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* multiphase contrast-enhanced abdominal CT covering the full abdominal region and corresponding radiology reports matched to the CT images\n\nExclusion Criteria:\n\n* CT images with poor diagnostic quality due to artifacts, including but not limited to: Convolution artifacts caused by improper arm positioning (e.g., arms placed alongside the body instead of above the head),Respiratory motion artifacts due to inadequate breath-holding.'}, 'identificationModule': {'nctId': 'NCT07040358', 'acronym': 'RADAR', 'briefTitle': 'Rapid Abdominal Diagnosis With AI & Radiology', 'organization': {'class': 'OTHER', 'fullName': 'First Affiliated Hospital of Zhejiang University'}, 'officialTitle': 'Development and Application of an AI Model for Accurate Interpretation of Abdominal Enhanced CT Images', 'orgStudyIdInfo': {'id': 'RADAR'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Internal Training Set'}, {'label': 'Internal Validation Set'}, {'label': 'External Test Set'}]}, 'contactsLocationsModule': {'locations': [{'zip': '310003', 'city': 'Hangzhou', 'state': 'Zhejiang', 'country': 'China', 'facility': 'the First Affliated Hospital, Zhejiang University School of Medicine', 'geoPoint': {'lat': 30.29365, 'lon': 120.16142}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'First Affiliated Hospital of Zhejiang University', 'class': 'OTHER'}, 'collaborators': [{'name': 'the First Division Hospital of Xinjiang Production and Construction Corps', 'class': 'UNKNOWN'}, {'name': "The First People's Hospital of Yuhang District", 'class': 'OTHER'}, {'name': 'Affiliated Hospital of Jiaxing University', 'class': 'OTHER'}, {'name': "Jixi County People's Hospital", 'class': 'UNKNOWN'}, {'name': "Anji County People's Hospital", 'class': 'UNKNOWN'}, {'name': 'Zhejiang University', 'class': 'OTHER'}, {'name': "People's Hospital of Beilun District, Ningbo City", 'class': 'UNKNOWN'}, {'name': "Haining People's Hospital", 'class': 'UNKNOWN'}, {'name': "Jingning County People's Hospital", 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Qi Zhang', 'investigatorAffiliation': 'First Affiliated Hospital of Zhejiang University'}}}}