AzTrials
AzTrials

Age ProblemNCT05901857

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

An observational study of Age Problem, run by Cairo University at 1 site in Egypt.

Unknown status

Status not confirmed. The sponsor has not verified this record for over two years, so it may be out of date.

Official record
Search all trials

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. Read more about this study

Who is it for?
Ages 6 to 16 years
Conditions the study is for
Age Problem
What is being tested?
convolutional neural network
How many people
22 participants
Where
1 siteEgypt
Study dates
30 Jun 2023to 1 Dec 2025
Phase
Observational
Are there results?
None posted in the registry

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 6 to 16 years
  • All sexes
  • Child

Study population: Panoramic radiographs will be recruited from Faculty of Dentistry, Oral Radiology Department.

Who may be able to join

  • 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.

Who cannot join

  • Patients with premature birth
  • Facial asymmetry
  • Congenital anomalies
  • History of trauma or surgery in dentofacial region

Trial locations and contacts

1 site in 1 country.

Rawan Elkassas contact

+201011385738

rawanelkassas@gmail.com

Egypt1 site, 1 recruiting

  • Rawan Elkassas

    Cairo

    rawan elkassas, rawan.elkassas@dentistry.cu.edu.eg

    Recruiting

Other trials in Egypt

About this study

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.

Read the detailed 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).

Conditions
Age Problem
Being tested
convolutional neural network

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • convolutional neural networkDiagnostic test

    A deep learning model for dental age classification from panoramic images

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Accuracy of dental age estimation from digital panoramic radiographs using CNN models

    Percentage

    Measured: Through study completion, an average of 1 year

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted13 Jun 2023
  2. Study start30 Jun 2023 (estimated)
  3. Primary completion1 Jan 2024 (estimated)
  4. Study completion1 Dec 2025 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Other
Time perspective
Retrospective
Enrolment
22 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT05901857
Sponsor's protocol ID
ORAD 3-3-1 (2)
Status last verified
Jun 2023
First submitted
12 May 2023
Last updated
13 Jun 2023
Results posted
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
Undecided

Publications

  • 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.

    PubMed 32239317

  • 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.

    PubMed 31160078

  • 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.

    PubMed 36394611

  • 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.

    PubMed 33410925

  • 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.

    PubMed 33661340

  • 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.

    PubMed 33441753

  • 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.

    PubMed 28918371

  • 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.

    PubMed 34911516

  • 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.

    PubMed 32012002

  • 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.

    PubMed 25195126

Questions about this trial

What is this clinical trial studying?

An observational study of Age Problem, run by Cairo University at 1 site in Egypt.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 5 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: status not confirmed, as of the sponsor's last update on 13 Jun 2023. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

Results are not available in the source record.

How can a doctor refer a patient or contact the research team?

Contact Rawan Elkassas (+201011385738, rawanelkassas@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Accuracy of dental age estimation from digital panoramic radiographs using CNN models (time frame: Through study completion, an average of 1 year). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT05901857, which also lists 10 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Jun 2023; AzTrials last refreshed its copy on 6 Nov 2026.

AzTrials uses cookies for analytics and advertising. You can accept or reject them; the site works either way. Privacy Policy.

Free account

Log in to keep searching

You’ve used your 3 free searches. AzTrials is free: log in or create a free account to keep searching clinical trials and save the ones you want to follow.

Create a free accountLog in