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

Study Overview
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Brief Summary
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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
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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).

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

Conditions
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Age Problem

Intervention / Treatment
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* Diagnostic Test: convolutional neural network


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 6 Years
Maximum Age: 16 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Diagnostic Test: convolutional neural network|A deep learning model for dental age classification from panoramic images|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Accuracy of dental age estimation from digital panoramic radiographs using CNN models | Percentage | Through study completion, an average of 1 year |