Ignite Creation Date:
2025-12-24 @ 3:36 PM
Ignite Modification Date:
2026-01-17 @ 8:40 PM
Study NCT ID:
NCT06945692
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-04-25
First Post:
2025-04-10
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Assessment of Accuracy and Aesthetics Following Automated Mandibular Defect Reconstruction Using AI
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D008339', 'term': 'Mandibular Neoplasms'}], 'ancestors': [{'id': 'D007573', 'term': 'Jaw Neoplasms'}, {'id': 'D012888', 'term': 'Skull Neoplasms'}, {'id': 'D001859', 'term': 'Bone Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D001847', 'term': 'Bone Diseases'}, {'id': 'D009140', 'term': 'Musculoskeletal Diseases'}, {'id': 'D007571', 'term': 'Jaw Diseases'}, {'id': 'D008336', 'term': 'Mandibular Diseases'}, {'id': 'D009057', 'term': 'Stomatognathic Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'TREATMENT', 'interventionModel': 'SINGLE_GROUP'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 4}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-05-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-04', 'completionDateStruct': {'date': '2026-05-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-04-20', 'studyFirstSubmitDate': '2025-04-10', 'studyFirstSubmitQcDate': '2025-04-20', 'lastUpdatePostDateStruct': {'date': '2025-04-25', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-04-25', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-04-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Accuracy Of the virtually Generated 3D model using AI', 'timeFrame': 'baseline', 'description': 'The measuring device is the AI model using the Percentage as a unit'}, {'measure': 'Accuracy of AI generated model clinically', 'timeFrame': 'baseline', 'description': 'The measuring device is by Superimposition of both virtual 3-d generated model and real patient CT post operative using software ( blender ) .\n\n( Structural Similarity Index) (SSIM)'}], 'secondaryOutcomes': [{'measure': 'Aethetic outcome', 'timeFrame': 'baseline', 'description': 'The measuring device is Facial appearance using a 4-point score'}, {'measure': 'Occlusion', 'timeFrame': 'baseline', 'description': 'The measuring device is Digital occlusion analysis using T-scan and the unit is percentage'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial intelligence', 'Patient specific reconstruction plates'], 'conditions': ['Mandibular Tumor']}, 'referencesModule': {'references': [{'pmid': '33009429', 'type': 'BACKGROUND', 'citation': 'Liang Y, Huan J, Li JD, Jiang C, Fang C, Liu Y. Use of artificial intelligence to recover mandibular morphology after disease. Sci Rep. 2020 Oct 2;10(1):16431. doi: 10.1038/s41598-020-73394-5.'}, {'pmid': '30115476', 'type': 'BACKGROUND', 'citation': 'van Baar GJC, Forouzanfar T, Liberton NPTJ, Winters HAH, Leusink FKJ. Accuracy of computer-assisted surgery in mandibular reconstruction: A systematic review. Oral Oncol. 2018 Sep;84:52-60. doi: 10.1016/j.oraloncology.2018.07.004. Epub 2018 Jul 20.'}]}, 'descriptionModule': {'briefSummary': 'The Aim of the study is to evaluate Accuracy of automated mandibular defect reconstruction using Artificial intelligence and assessing impact on aesthetic and occlusion outcomes using patient-specific reconstruction plates.', 'detailedDescription': 'The digital surgical process often requires an expected mandibular reference model. Currently, the common digital surgery process, is to mirror repair or manually look for other similar mandibles for local data fusion and smoothing processing. A more accurate expected reference model is difficult to achieve, time consuming and difficult to promote in clinical practice. Moreover, rapid routing processing often has poor accuracy. For cumulative bilateral lesions, massive lesions, obvious displacement or lesions cross the middle line, there is still no effective method to predict the expected reference model in clinical practice.\n\nThe main objective for conducting this study is to propose an improved algorithm to overcome the drawbacks of recent studies using 3D Unet and to test the predictability and clinical value of virtually generated 3d models of defected mandible in real patients.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT'], 'maximumAge': '55 Years', 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients with mandibular tumors, cysts or any benign disease resulting in mandibular continuity defect.\n* Age group: from 18 - 55 years old.\n* No sex predilection.\n* CTs or CBCTs of only healthy mandibles from an online database and real data.\n\nExclusion Criteria:\n\n* Patients with mandibular malignant lesions.\n* Children age group from 2-17.\n* CTs Of maxilla.\n* Elderly patients to be excluded due to the normal physiologic bony change.'}, 'identificationModule': {'nctId': 'NCT06945692', 'briefTitle': 'Assessment of Accuracy and Aesthetics Following Automated Mandibular Defect Reconstruction Using AI', 'organization': {'class': 'OTHER', 'fullName': 'Cairo University'}, 'officialTitle': 'Assessment of Accuracy and Aesthetics Following Automated Mandibular Defect Reconstruction Using Artificial Intelligence: A Case Series Study', 'orgStudyIdInfo': {'id': 'AI in Mandibular Defects'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Patient specific reconstruction plates', 'description': 'Patients with benign lesions indicated for resection and resulting in mandibular continuity defects treated with patient specific reconstruction plates.', 'interventionNames': ['Procedure: patient specific reconstruction plates']}], 'interventions': [{'name': 'patient specific reconstruction plates', 'type': 'PROCEDURE', 'description': 'Use of patient specific reconstruction plates on the 3-D virtually-generated defect using Artificial Intelligence.', 'armGroupLabels': ['Patient specific reconstruction plates']}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Sarah Moustafa. Moustafa, MSc.', 'role': 'CONTACT', 'email': 'sarah.elayoutti@dentistry.cu.edu.eg', 'phone': '56794540'}, {'name': 'Sarah Moustafa. Moustafa, PHD', 'role': 'CONTACT', 'email': 'waleed.elbeialy@dentistry.cu.edu.eg', 'phone': '01006133135'}], 'overallOfficials': [{'name': 'Sarah Moustafa, MSc.', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Cairo University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Cairo University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Assistant lecturer, oral and maxillofacial surgery department, Ahram Canadian University, PHD Student at Cairo university.', 'investigatorFullName': 'Sarah Moustafa Mahmoud Fahmy El-Youtti', 'investigatorAffiliation': 'Cairo University'}}}}