Ignite Creation Date:
2025-12-25 @ 2:07 AM
Ignite Modification Date:
2025-12-27 @ 11:24 PM
Study NCT ID:
NCT06712160
Status:
COMPLETED
Last Update Posted:
2024-12-02
First Post:
2024-11-21
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Diagnostic Accuracy of Artificial Intelligence, CBCT, and Clinical Examination in Detecting Number of Root Canals in Conventional and Retreated Maxillary and Mandibular Molars
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'interventionBrowseModule': {'meshes': [{'id': 'D001185', 'term': 'Artificial Intelligence'}], 'ancestors': [{'id': 'D000465', 'term': 'Algorithms'}, {'id': 'D055641', 'term': 'Mathematical Concepts'}]}}, 'documentSection': {'largeDocumentModule': {'largeDocs': [{'date': '2024-10-29', 'size': 133765, 'label': 'Study Protocol', 'hasIcf': False, 'hasSap': False, 'filename': 'Prot_000.pdf', 'typeAbbrev': 'Prot', 'uploadDate': '2024-11-28T04:55', 'hasProtocol': True}, {'date': '2024-10-29', 'size': 110786, 'label': 'Statistical Analysis Plan', 'hasIcf': False, 'hasSap': True, 'filename': 'SAP_001.pdf', 'typeAbbrev': 'SAP', 'uploadDate': '2024-11-28T04:55', 'hasProtocol': False}, {'date': '2024-10-29', 'size': 164008, 'label': 'Informed Consent Form', 'hasIcf': True, 'hasSap': False, 'filename': 'ICF_002.pdf', 'typeAbbrev': 'ICF', 'uploadDate': '2024-11-28T04:56', 'hasProtocol': False}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'SINGLE_GROUP'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 212}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2023-01-20', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-10', 'completionDateStruct': {'date': '2024-02-20', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2024-11-28', 'studyFirstSubmitDate': '2024-11-21', 'studyFirstSubmitQcDate': '2024-11-28', 'lastUpdatePostDateStruct': {'date': '2024-12-02', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-12-02', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-01-20', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'The number of canals detected', 'timeFrame': '1 day', 'description': 'The number of canals detected clinically using DOM, CBCT and by AI'}], 'secondaryOutcomes': [{'measure': 'Canal Morphology', 'timeFrame': '1 day', 'description': 'Canal morphology for successful and failed cases'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['CBCT', 'AI'], 'conditions': ['Number of Root Canals']}, 'descriptionModule': {'briefSummary': 'The study compares the effectiveness of Artificial Intelligence (AI), CBCT, and clinical examination in detecting root canals in upper first, upper second, and lower first molars. Results show AI detects more molars with three or four canals in conventional treatment cases and retreatment cases.', 'detailedDescription': 'Introduction: Accurate root canal detection is crucial for successful endodontic treatment, particularly in complex molar cases. Conventional methods, such as clinical examination and cone-beam computed tomography (CBCT), have their limitations, as high radiation exposure. Recent advancements in Artificial Intelligence (AI) have shown promise in improving diagnostic accuracy. This study aims to compare the effectiveness of AI, CBCT, and clinical examination using a dental operating microscope (DOM) in detecting root canals in upper first, upper second, and lower first molars, in both conventional and retreatment cases. Methods: CBCT scans from 210 patients requiring non-surgical root canal therapy or re-treatment were selected. The scans were analyzed using three detection methods: clinical examination via DOM, interpretation by two experienced endodontists using CBCT, and an AI convolutional neural network (CNN) software (Diagnocat). The detected number of root canals was recorded and compared across the three methods.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT'], 'maximumAge': '40 Years', 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Male and female patients who were capable of providing informed consent\n* Age between 18 to 40 years old.\n* A restorable tooth.\n\nExclusion Criteria:\n\n* Patients that underwent vital pulp therapies.\n* Patients with calcifications in pulp space.\n* Open apex/immature roots.\n* Teeth restored by full coverage crowns.\n* Pregnant women by taking adequate history from patient and pregnancy test that was done in the first visit'}, 'identificationModule': {'nctId': 'NCT06712160', 'briefTitle': 'Diagnostic Accuracy of Artificial Intelligence, CBCT, and Clinical Examination in Detecting Number of Root Canals in Conventional and Retreated Maxillary and Mandibular Molars', 'organization': {'class': 'OTHER', 'fullName': 'Misr International University'}, 'officialTitle': 'Diagnostic Accuracy of Artificial Intelligence, CBCT, and Clinical Examination in Detecting Number of Root Canals in Conventional and Retreated Maxillary and Mandibular Molars', 'orgStudyIdInfo': {'id': 'MIU-IRB-2425-008'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'CBCT, Clinical using DOM', 'description': 'Comparing the three methods for the detection of the number of canals of maxillary and mandibular molars', 'interventionNames': ['Diagnostic Test: Artificial Intelligence']}], 'interventions': [{'name': 'Artificial Intelligence', 'type': 'DIAGNOSTIC_TEST', 'description': 'The number of canals detected by AI', 'armGroupLabels': ['CBCT, Clinical using DOM']}]}, 'contactsLocationsModule': {'locations': [{'zip': '00202', 'city': 'Cairo', 'country': 'Egypt', 'facility': 'Misr International University', 'geoPoint': {'lat': 30.06263, 'lon': 31.24967}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'will be shared after publishing the paper'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Misr International University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}