Ignite Creation Date:
2025-12-24 @ 9:18 PM
Ignite Modification Date:
2026-01-01 @ 8:07 AM
Study NCT ID:
NCT06803004
Status:
COMPLETED
Last Update Posted:
2025-08-28
First Post:
2025-01-20
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Diagnostic Efficacy Study of AI System in Screening Infants With Developmental Dysplasia of the Hip
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D000082602', 'term': 'Developmental Dysplasia of the Hip'}], 'ancestors': [{'id': 'D006617', 'term': 'Hip Dislocation'}, {'id': 'D004204', 'term': 'Joint Dislocations'}, {'id': 'D007592', 'term': 'Joint Diseases'}, {'id': 'D009140', 'term': 'Musculoskeletal Diseases'}, {'id': 'D009139', 'term': 'Musculoskeletal Abnormalities'}, {'id': 'D000013', 'term': 'Congenital Abnormalities'}, {'id': 'D009358', 'term': 'Congenital, Hereditary, and Neonatal Diseases and Abnormalities'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'DOUBLE', 'whoMasked': ['INVESTIGATOR', 'OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 1789}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2025-07-18', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-08', 'completionDateStruct': {'date': '2025-08-20', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-08-22', 'studyFirstSubmitDate': '2025-01-20', 'studyFirstSubmitQcDate': '2025-01-26', 'lastUpdatePostDateStruct': {'date': '2025-08-28', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-01-31', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-08-01', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Average diagnostic accuracy', 'timeFrame': 'up to 4 weeks', 'description': "It is calculated by dividing the number of preliminary interpretations that are consistent with the expert team's grading by the total number of cases that should be diagnosed."}], 'secondaryOutcomes': [{'measure': 'Average diagnostic sensitivity', 'timeFrame': 'up to 4 weeks', 'description': 'It is calculated by dividing the number of true positive cases by the sum of true positive cases and false negative cases.'}, {'measure': 'Average diagnostic specificity', 'timeFrame': 'up to 4 weeks', 'description': 'It is calculated by dividing the number of true negative cases by the sum of true negative cases and false positive cases.'}, {'measure': 'Average times of follow-up visits', 'timeFrame': 'up to 4 weeks', 'description': 'The average number of follow-up visits for each group is obtained by dividing the total number of follow-up visits for each participating infant within the group by the number of participating infants in that group.'}, {'measure': 'Diagnosis time', 'timeFrame': 'up to 4 weeks', 'description': 'Time taken to make ultrasound diagnosis in each group'}, {'measure': "Bang's index", 'timeFrame': 'up to 4 weeks', 'description': "Bang's index is used to evaluate whether the blind method is implemented successfully"}, {'measure': 'Frequency pediatrician adjusts preliminary annotation', 'timeFrame': 'up to 4 weeks', 'description': 'Proportion of studies the annotation is changed'}, {'measure': 'Frequency pediatrician adjusts preliminary DDH type', 'timeFrame': 'up to 4 weeks', 'description': 'Proportion of studies the DDH type is changed in final report'}, {'measure': 'Mean change in alpha angle between preliminary and final report', 'timeFrame': 'up to 4 weeks', 'description': 'Average change in alpha angle between preliminary and final report'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Hip Dysplasia, Developmental']}, 'descriptionModule': {'briefSummary': 'To ascertain the efficacy of the DeepDDH system, a deep learning framework, in enhancing diagnostic accuracy and curtailing follow-up intervals for infants undergoing screening for developmental dysplasia of the hip (DDH), the researchers are executing a blinded, randomized controlled trial. This trial juxtaposes AI-only and AI-assisted assessments of DDH against sonographer interpretations across various proficiency levels in the preliminary analysis of ultrasound images.', 'detailedDescription': '1. Participating centers and doctors:\n\n The data in the ultrasound screening sequence database in this part of the study were mainly from Renji Hospital and the Sixth People\'s Hospital in Shanghai between August 2014 and December 2021. Renji Hospital, the Sixth People\'s Hospital, and the Pediatric Hospital Affiliated to Fudan University, three top-three hospitals in Shanghai, started Graf ultrasound examination earlier, with an average history of more than 10 years. And they are responsible for providing expert sonographers with more than 5-10 years of DDH ultrasound diagnosis experience, and pediatric orthopedic experts with 5-10 years of DDH diagnosis experience to participate in the study. However, several other primary or remote medical institutions with late DDH ultrasound screening and insufficient diagnostic experience were mainly responsible for providing primary sonographers to participate in the study. Before the study, the sonographers involved in this study will be evaluated uniformly and quantitatively through examination papers.\n2. Research process:\n\nOne week before the start of the study, the sonographers registered in the study received uniform training of the latest DDH ultrasound diagnosis in the form of PPT, video, literature study, and offline instruction.\n\nFor the included cases in the ultrasound screening sequence database, they would appear in different control groups in a random form, such as the AI model, the Expert sonographer group, the primary sonographer group, and the primary sonographer with AI \'aid group. All cases in the ultrasound screening sequence database were stratified and block-randomized into the above four groups (primary, experts, AI-independent, AI-assisted primary).\n\nIn the AI-assisted group, each sonographer was asked to choose whether to modify or confirm the diagnosis according to the measurement marks, diagnostic angles and typing results provided by the AI device. However, in the Expert sonographer group and junior sonographer unassisted group, the dedicated research assistant will turn off the AI display function to ensure that no additional information is provided to the sonographer. The consensus of two pediatric orthopedic expert with 5-10 years of experience in DDH ultrasound diagnosis was used as the gold standard. In case of disagreement, a third pediatric expert will evaluate the diagnosis results of DDH. The final consensus was used as the gold standard.\n\nThen, the pediatric orthopedic expert group were given the initial annotations diagnosis results of DDH in the above four groups, including diagnostic images, diagnostic measurement marks, diagnostic angles and diagnostic types. And by reviewing the initial annotations, selecting "confirm" or "modify" the initial annotations, the final annotations are made again for those who need to be modified, and the final report results are obtained.\n\nFinally, the operation results of the above different groups were summarized and analyzed by independent research assistants, including α Angle, β Angle, typing results, and the specific follow-up experience of the case including follow-up times, diagnosis time, Bang\'s index, proportion of studies the annotation is changed, proportion of studies the DDH type is changed in final report, and mean change in alpha angle between preliminary and final report.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD'], 'maximumAge': '6 Months', 'minimumAge': '28 Days', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Infants underwent DDH ultrasound examinations.\n* Infants aged 28 days to 6 months.\n\nExclusion Criteria:\n\n* Infants with lacking or incomplete ultrasound images.\n* Infants with poor image quality, including non-compliance with anatomical identification and usability check.\n* Infants with hip dysplasia caused by other diseases.'}, 'identificationModule': {'nctId': 'NCT06803004', 'acronym': 'ASIDDH', 'briefTitle': 'Diagnostic Efficacy Study of AI System in Screening Infants With Developmental Dysplasia of the Hip', 'organization': {'class': 'OTHER', 'fullName': 'RenJi Hospital'}, 'officialTitle': 'Blinded Randomized Control Trail of Artificial Intelligence-Assisted Ultrasound Screening for Neonatal Hip Dysplasia in a Clinical Cohort', 'orgStudyIdInfo': {'id': 'LY2025-004-C'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'ACTIVE_COMPARATOR', 'label': 'Junior Sonographer Annotation', 'description': "Participants will not receive visual cues from the DeepDDH system. Junior sonographer technicians will offer preliminary interpretations before these are subjected to validation and subsequent review by expert's team.", 'interventionNames': ['Other: Junior sonographer measurement of DDH']}, {'type': 'ACTIVE_COMPARATOR', 'label': 'Senior Sonographer Annotation', 'description': "Participants will not receive visual cues from the DeepDDH system. Senior sonographer technicians will offer preliminary interpretations before these are subjected to validation and subsequent review by expert's team.", 'interventionNames': ['Other: Senior sonographer measurement of DDH']}, {'type': 'EXPERIMENTAL', 'label': 'DeepDDH system Annotation', 'description': 'Through randomization, a subset of the preliminary interpretations will be conducted by AI technology, and the study team will evaluate the degree of divergence between these AI-generated preliminary interpretations and the final interpretations.', 'interventionNames': ['Other: Automated annotation of the DDH measurement through deep learning']}, {'type': 'EXPERIMENTAL', 'label': 'DeepDDH-assist Junior Sonographer Annotation', 'description': 'Participants will receive visual cues from the DeepDDH system.', 'interventionNames': ['Other: AI-assisted junior sonographer measurement of DDH']}], 'interventions': [{'name': 'Junior sonographer measurement of DDH', 'type': 'OTHER', 'description': "Participants will not receive visual cues from the DeepDDH system. Junior sonographer technicians will offer preliminary interpretations before these are subjected to validation and subsequent review by expert's team.", 'armGroupLabels': ['Junior Sonographer Annotation']}, {'name': 'Senior sonographer measurement of DDH', 'type': 'OTHER', 'description': "Participants will not receive visual cues from the DeepDDH system. Senior sonographer technicians will offer preliminary interpretations before these are subjected to validation and subsequent review by expert's team.", 'armGroupLabels': ['Senior Sonographer Annotation']}, {'name': 'Automated annotation of the DDH measurement through deep learning', 'type': 'OTHER', 'description': 'Through randomization, a subset of the preliminary interpretations will be conducted by AI technology, and the study team will evaluate the degree of divergence between these AI-generated preliminary interpretations and the final interpretations.', 'armGroupLabels': ['DeepDDH system Annotation']}, {'name': 'AI-assisted junior sonographer measurement of DDH', 'type': 'OTHER', 'description': 'Participants will receive visual cues from the DeepDDH system.', 'armGroupLabels': ['DeepDDH-assist Junior Sonographer Annotation']}]}, 'contactsLocationsModule': {'locations': [{'zip': '200127', 'city': 'Shanghai', 'state': 'Shanghai Municipality', 'country': 'China', 'facility': 'Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'CSR', 'ANALYTIC_CODE'], 'ipdSharing': 'YES', 'description': 'The clinical demographic information of the subjects was recorded through the Case Record Form, and the data will be collected and managed electronically through the ResMan platform.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'RenJi Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Director of Department of ultrasound in medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine', 'investigatorFullName': 'Lixin Jiang', 'investigatorAffiliation': 'RenJi Hospital'}}}}