Assessing the Precision of Convolutional Neural Networks for Dental Age Estimation From Panoramic Radiographs

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05901857
• Other study ID #: ORAD 3-3-1 (2)
• Status: Recruiting
• Start date: June 30, 2023
• Est. completion date: December 1, 2025

Study information

• Verified date: June 2023
• Source: Cairo University
• Contact: Rawan Elkassas
• Phone: +201011385738
• Email: rawanelkassas[@]gmail.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Description:

Willems method is a dental age estimation technique modified from Demirjian method by creating new tables from which a maturity score is directly expressed in years. Panoramic 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. The 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). The 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).

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 22
• Est. completion date: December 1, 2025
• Est. primary completion date: January 1, 2024
• 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

Related Conditions & MeSH terms

• Age Problem

Intervention

Diagnostic Test:
• convolutional neural network
A deep learning model for dental age classification from panoramic images

Locations

Country	Name	City	State
Egypt	Rawan Elkassas	Cairo

Sponsors (1)

Lead Sponsor	Collaborator
Cairo University

Country where clinical trial is conducted

Egypt, 

References & Publications (10)

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

Outcome

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