Age Problem Clinical Trial
Official title:
Assessing the Precision of Convolutional Neural Networks for Dental Age Estimation in an Egyptian Population From Digital Panoramic Radiographs: A Diagnostic Accuracy Study
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.
Status | Recruiting |
Enrollment | 22 |
Est. completion date | December 1, 2025 |
Est. primary completion date | January 1, 2024 |
Accepts healthy volunteers | |
Gender | All |
Age group | 6 Years to 16 Years |
Eligibility | Inclusion Criteria: - Presence of all mandibular left permanent teeth (except third molars) - Clearly visible root development - No systemic disease - No history of root canal therapy or extraction - No related diseases affecting mandibular development such as cysts or tumors. Exclusion Criteria: - Patients with premature birth - Facial asymmetry - Congenital anomalies - History of trauma or surgery in dentofacial region |
Country | Name | City | State |
---|---|---|---|
Egypt | Rawan Elkassas | Cairo |
Lead Sponsor | Collaborator |
---|---|
Cairo University |
Egypt,
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. — View 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. — View 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. — View 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. — View 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. — View 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. Erratum In: Sci Rep. 2022 Feb 7;12(1):2332. — View 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. — View 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. — View 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. — View 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. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Accuracy of dental age estimation from digital panoramic radiographs using CNN models | Percentage | Through study completion, an average of 1 year |
Status | Clinical Trial | Phase | |
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