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

Assessing the Precision of Convolutional Neural Networks for Dental Age Estimation in an Egyptian Population From Digital Panoramic Radiographs: A Diagnostic Accuracy Study

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05901857
Enrollment
22
Registered
2023-06-13
Start date
2023-06-30
Completion date
2025-12-01
Last updated
2023-06-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Age Problem

Brief 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.

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).

Interventions

A deep learning model for dental age classification from panoramic images

Sponsors

Cairo University
Lead SponsorOTHER

Study design

Observational model
OTHER
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
6 Years to 16 Years

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

Design outcomes

Primary

MeasureTime frameDescription
Accuracy of dental age estimation from digital panoramic radiographs using CNN modelsThrough study completion, an average of 1 yearPercentage

Countries

Egypt

Contacts

Primary ContactRawan Elkassas
rawanelkassas@gmail.com+201011385738

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026