Age group prediction at the 18-year-old threshold using radiographic images of the mandibular third molar of a Thai population: A comparison between human-based and deep learning-based methods

Completed

• Conditions: Dental age estimation using deep learning; Dental age estimation; Deep learning; Mandibular third molar; Forensic Odontology; Demirjian method

• Registration Number: TCTR20230519002
• Lead Sponsor: Faculty of Dentistry, Prince of Songkla University

Brief Summary

Not available

Detailed Description

Not available

Study Timeline

• Study Completion: 2018-12-31
• Study Start: 2023-05-19
• Last Update Posted: 19 August 2024

Recruitment & Eligibility

• Status: Completed
• Sex: All
• Target Recruitment: 1872

Inclusion Criteria

Patients with available data on the birth date and date of radiographic examination

Exclusion Criteria

1. Patients whose radiographic images had poor quality,
2. Patients with missing or malaligned mandibular third molars (severe buccoversion or linguoversion),
3. Patients who had developmental anomalies, jawbone pathology, or syndromes that affected the dental development

Study & Design

• Study Type: Observational
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Accuracy of age-group prediction After the model's training phase Percentage of accuracy

Secondary Outcome Measures

Name	Time	Method
/A N/A N/A