Ignite Creation Date:
2025-12-24 @ 5:35 PM
Ignite Modification Date:
2025-12-29 @ 2:20 AM
Study NCT ID:
NCT07162168
Status:
RECRUITING
Last Update Posted:
2025-12-03
First Post:
2025-08-29
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Automated Bone Age Estimation From Noncontrast Abdominal CT Using Deep Learning
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': 3000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2024-09-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-11', 'completionDateStruct': {'date': '2027-12-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-11-25', 'studyFirstSubmitDate': '2025-08-29', 'studyFirstSubmitQcDate': '2025-08-29', 'lastUpdatePostDateStruct': {'date': '2025-12-03', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-09-09', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2027-09-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Radiomics-Based Bone Age Prediction Model', 'timeFrame': 'Retrospective analysis of CT scans acquired between Sep 01.2024 to Oct 01.2025', 'description': 'Extraction of radiomics features from abdominal CT images of the proximal femur and development of a machine learning model to estimate biological bone age. The performance of the model will be evaluated by comparing predicted bone age with chronological age.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Bone Aging', 'Osteoporosis Diagnosis']}, 'descriptionModule': {'briefSummary': 'This study is a retrospective analysis that uses abdominal CT scans, which were originally taken for other medical reasons, to estimate bone age. By applying advanced deep learning methods, the investigators aim to develop a tool that can evaluate bone health and detect early signs of osteoporosis without requiring additional scans or radiation. This approach may help doctors better understand bone aging, improve screening for bone weakness, and provide patients with more personalized information about their bone health.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'This retrospective study included about 3,000 adult participants (aged over 18 years) who underwent noncontrast abdominal CT scans that fully covered the proximal femur across multiple regions in China. Participants with poor image quality, prior hip surgery or internal fixation, bone tumors, severe hip deformities, or prior proximal femur fractures were excluded. All scans were performed for non-orthopedic indications. The study was approved by the Institutional Ethics Committee (approval number: 2024PHB388-001).', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Adults aged over 18 years.\n* Underwent routine noncontrast abdominal CT scans.\n* CT scans fully included the proximal femur.\n* Scans were performed for non-orthopedic clinical indications.\n* Provided necessary demographic information (e.g., age, sex).\n\nExclusion Criteria:\n\n* CT scans with poor image quality or severe artifacts that precluded accurate analysis.\n* History of hip surgery or presence of internal fixation devices.\n* Presence of bone tumors in the proximal femur.\n* Severe hip deformity or prior fractures affecting the proximal femur.\n* Pediatric patients or pregnant individuals (if applicable).'}, 'identificationModule': {'nctId': 'NCT07162168', 'briefTitle': 'Automated Bone Age Estimation From Noncontrast Abdominal CT Using Deep Learning', 'organization': {'class': 'OTHER', 'fullName': "Peking University People's Hospital"}, 'officialTitle': 'Development and Evaluation of a Deep Learning-Based Model for Automated Osteoporosis Assessment Using CT Images', 'orgStudyIdInfo': {'id': '2024PHB388-001'}}, 'armsInterventionsModule': {'armGroups': [{'label': "Peking University People's Hospital cohort", 'description': 'No intervention'}, {'label': 'Shandong Cohort', 'description': 'No intervention'}, {'label': 'Canton Cohort', 'description': 'No intervention'}, {'label': 'Guizhou cohort', 'description': 'No intervention'}, {'label': 'Hunan Cohort', 'description': 'No intervention'}, {'label': 'Inner Mongolia Cohort', 'description': 'No intervention'}, {'label': 'Shaanxi Cohort', 'description': 'No intervention'}, {'label': 'Shandong Cohort2', 'description': 'No intervention'}, {'label': 'Other province Cohort', 'description': 'No intervention'}]}, 'contactsLocationsModule': {'locations': [{'city': 'Beijing', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'yuhui Kou, M.D', 'role': 'CONTACT', 'email': 'yuhuikou@bjmu.edu.cn', 'phone': '86-13146213332'}], 'facility': 'CT machine', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}], 'centralContacts': [{'name': 'hanwen Cheng, M.D', 'role': 'CONTACT', 'email': 'chenghanwen1998@126.com', 'phone': '86-19541080926'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'Our Research has not been finished yet.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': "Peking University People's Hospital", 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Research Fellow', 'investigatorFullName': 'Yuhui Kou', 'investigatorAffiliation': "Peking University People's Hospital"}}}}