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

June 4, 2023 updated by: Rawan Abdel Wahhab Bahaa Eldin Elkassas, Cairo University

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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).

Study Type

Observational

Enrollment (Estimated)

22

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child

Accepts Healthy Volunteers

N/A

Sampling Method

Probability Sample

Study Population

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

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Cairo University

Investigators

  • Study Chair: Mohab Eid, Nile University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

June 30, 2023

Primary Completion (Estimated)

January 1, 2024

Study Completion (Estimated)

December 1, 2025

Study Registration Dates

First Submitted

May 12, 2023

First Submitted That Met QC Criteria

June 4, 2023

First Posted (Actual)

June 13, 2023

Study Record Updates

Last Update Posted (Actual)

June 13, 2023

Last Update Submitted That Met QC Criteria

June 4, 2023

Last Verified

June 1, 2023

More Information

Terms related to this study

Other Study ID Numbers

  • ORAD 3-3-1 (2)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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