Ignite Creation Date:
2025-12-24 @ 7:03 PM
Ignite Modification Date:
2026-01-04 @ 9:14 AM
Study NCT ID:
NCT05901857
Status:
UNKNOWN
Last Update Posted:
2023-06-13
First Post:
2023-05-12
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Assessing the Precision of Convolutional Neural Networks for Dental Age Estimation From Panoramic Radiographs
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'interventionBrowseModule': {'meshes': [{'id': 'D000098415', 'term': 'Convolutional Neural Networks'}], 'ancestors': [{'id': 'D016571', 'term': 'Neural Networks, Computer'}, {'id': 'D055641', 'term': 'Mathematical Concepts'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 22}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'RECRUITING', 'startDateStruct': {'date': '2023-06-30', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-06', 'completionDateStruct': {'date': '2025-12-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2023-06-04', 'studyFirstSubmitDate': '2023-05-12', 'studyFirstSubmitQcDate': '2023-06-04', 'lastUpdatePostDateStruct': {'date': '2023-06-13', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-06-13', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-01-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Accuracy of dental age estimation from digital panoramic radiographs using CNN models', 'timeFrame': 'Through study completion, an average of 1 year', 'description': 'Percentage'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Age Problem']}, 'referencesModule': {'references': [{'pmid': '32239317', 'type': 'BACKGROUND', 'citation': 'Banar N, Bertels J, Laurent F, Boedi RM, De Tobel J, Thevissen P, Vandermeulen D. Towards fully automated third molar development staging in panoramic radiographs. Int J Legal Med. 2020 Sep;134(5):1831-1841. doi: 10.1007/s00414-020-02283-3. Epub 2020 Apr 1.'}, {'pmid': '31160078', 'type': 'BACKGROUND', 'citation': 'Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. Deep Learning for the Radiographic Detection of Apical Lesions. J Endod. 2019 Jul;45(7):917-922.e5. doi: 10.1016/j.joen.2019.03.016. Epub 2019 Jun 1.'}, {'pmid': '36394611', 'type': 'BACKGROUND', 'citation': 'El-Desouky SS, Kabbash IA. Age estimation of children based on open apex measurement in the developing permanent dentition: an Egyptian formula. Clin Oral Investig. 2023 Apr;27(4):1529-1539. doi: 10.1007/s00784-022-04773-7. Epub 2022 Nov 17.'}, {'pmid': '33410925', 'type': 'BACKGROUND', 'citation': "Galibourg A, Cussat-Blanc S, Dumoncel J, Telmon N, Monsarrat P, Maret D. Comparison of different machine learning approaches to predict dental age using Demirjian's staging approach. Int J Legal Med. 2021 Mar;135(2):665-675. doi: 10.1007/s00414-020-02489-5. Epub 2021 Jan 7."}, {'pmid': '33661340', 'type': 'BACKGROUND', 'citation': 'Guo YC, Han M, Chi Y, Long H, Zhang D, Yang J, Yang Y, Chen T, Du S. Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images. Int J Legal Med. 2021 Jul;135(4):1589-1597. doi: 10.1007/s00414-021-02542-x. Epub 2021 Mar 4.'}, {'pmid': '33441753', 'type': 'BACKGROUND', 'citation': 'Kim S, Lee YH, Noh YK, Park FC, Auh QS. Age-group determination of living individuals using first molar images based on artificial intelligence. Sci Rep. 2021 Jan 13;11(1):1073. doi: 10.1038/s41598-020-80182-8.'}, {'pmid': '28918371', 'type': 'BACKGROUND', 'citation': 'Sehrawat JS, Singh M. Willems method of dental age estimation in children: A systematic review and meta-analysis. J Forensic Leg Med. 2017 Nov;52:122-129. doi: 10.1016/j.jflm.2017.08.017. Epub 2017 Aug 25.'}, {'pmid': '34911516', 'type': 'BACKGROUND', 'citation': 'Shen S, Liu Z, Wang J, Fan L, Ji F, Tao J. Machine learning assisted Cameriere method for dental age estimation. BMC Oral Health. 2021 Dec 15;21(1):641. doi: 10.1186/s12903-021-01996-0.'}, {'pmid': '32012002', 'type': 'BACKGROUND', 'citation': 'Vila-Blanco N, Carreira MJ, Varas-Quintana P, Balsa-Castro C, Tomas I. Deep Neural Networks for Chronological Age Estimation From OPG Images. IEEE Trans Med Imaging. 2020 Jul;39(7):2374-2384. doi: 10.1109/TMI.2020.2968765. Epub 2020 Jan 31.'}, {'pmid': '25195126', 'type': 'BACKGROUND', 'citation': 'Ye X, Jiang F, Sheng X, Huang H, Shen X. Dental age assessment in 7-14-year-old Chinese children: comparison of Demirjian and Willems methods. Forensic Sci Int. 2014 Nov;244:36-41. doi: 10.1016/j.forsciint.2014.07.027. Epub 2014 Aug 19.'}]}, 'descriptionModule': {'briefSummary': 'The aim of this study is to assess the accuracy of a convolutional neural network in dental age estimation from digital panoramic radiographs. The reference standard will be the chronological age of the patient.', 'detailedDescription': "Willems method is a dental age estimation technique modiļ¬ed from Demirjian method by creating new tables from which a maturity score is directly expressed in years.\n\nPanoramic radiographs of all participants will be taken with their informed consent, then they will be numbered and coded. Chronological age for each participant will be calculated by subtracting date of birth from date of radiograph and the real age will be blinded from the researcher (The chronological age is the ground truth). All panoramic radiographs will be examined twice by the main author to determine the dental age according to Willems method.\n\nThe seven mandibular left teeth excluding the third molar will be scored as '0' for absence of calcification, and 'A' to 'H', depending on the stage of calcification. Each letter corresponds to a score which is the dental age fraction using tables for boys and girls. Summing the scores for the seven left mandibular teeth directly will result in the estimated dental age. The dental radiologist estimation accurancy will be compared to the ground truth (first index test).\n\nThe second index test which will also be compared to the ground truth is the CNN model. To prepare the dataset for the CNN model, a rigorous preprocessing procedure will be followed. This will involve resizing the images to the desired dimensions, segmenting the teeth parts to be included in the image, and applying data augmentation techniques to enhance the quality and quantity of the dataset. The dataset will then be split into training and testing sets using a 20:80 ratio, which will be carefully selected based on the expected number of samples. Also the accuracy of the model will be assessed compared to the ground truth (the chronological ages)."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD'], 'maximumAge': '16 Years', 'minimumAge': '6 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Panoramic radiographs will be recruited from Faculty of Dentistry, Oral Radiology Department.', 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Presence of all mandibular left permanent teeth (except third molars)\n* Clearly visible root development\n* No systemic disease\n* No history of root canal therapy or extraction\n* No related diseases affecting mandibular development such as cysts or tumors.\n\nExclusion Criteria:\n\n* Patients with premature birth\n* Facial asymmetry\n* Congenital anomalies\n* History of trauma or surgery in dentofacial region'}, 'identificationModule': {'nctId': 'NCT05901857', 'briefTitle': 'Assessing the Precision of Convolutional Neural Networks for Dental Age Estimation From Panoramic Radiographs', 'organization': {'class': 'OTHER', 'fullName': 'Cairo University'}, 'officialTitle': 'Assessing the Precision of Convolutional Neural Networks for Dental Age Estimation in an Egyptian Population From Digital Panoramic Radiographs: A Diagnostic Accuracy Study', 'orgStudyIdInfo': {'id': 'ORAD 3-3-1 (2)'}}, 'armsInterventionsModule': {'interventions': [{'name': 'convolutional neural network', 'type': 'DIAGNOSTIC_TEST', 'description': 'A deep learning model for dental age classification from panoramic images'}]}, 'contactsLocationsModule': {'locations': [{'city': 'Cairo', 'status': 'RECRUITING', 'country': 'Egypt', 'contacts': [{'name': 'rawan elkassas', 'role': 'CONTACT', 'email': 'rawan.elkassas@dentistry.cu.edu.eg'}], 'facility': 'Rawan Elkassas', 'geoPoint': {'lat': 30.06263, 'lon': 31.24967}}], 'centralContacts': [{'name': 'Rawan Elkassas', 'role': 'CONTACT', 'email': 'rawanelkassas@gmail.com', 'phone': '+201011385738'}], 'overallOfficials': [{'name': 'Mohab Eid', 'role': 'STUDY_CHAIR', 'affiliation': 'Nile University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Cairo University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Rawan Abdel Wahhab Bahaa Eldin Elkassas', 'investigatorAffiliation': 'Cairo University'}}}}