Defense Date
2026
Document Type
Thesis
Degree Name
Master of Science in Dentistry
Department
Dentistry
First Advisor
Dr. Garry Myers
Second Advisor
Dr. Caroline Carrico
Third Advisor
Dr. Anusha Vaddi
Abstract
Objective: The aim of this study was to assess the performance of an Artificial Intelligence (AI) program (OverJet AI) with that of experienced endodontic residents, endodontists, a radiologist, and dental students in detecting periapical radiolucencies using radiographic images.
Methods: One hundred and fifty periapical radiographic images were analyzed using the OverJet AI software to determine the presence/absence of a periapical radiolucency as well as the size (small 1-3mm, medium 4-6mm, and large 6+mm). The unaltered images were also evaluated by two endodontic residents, two practicing endodontists, an oral radiologist and two 4th year dental students.
Results: Agreement between the AI system and human observers was comparable to interobserver agreement among clinicians, with kappa values indicating moderate to substantial agreement for both detection and size classification of periapical radiolucencies. Additionally, the AI demonstrated strong diagnostic performance with 87% sensitivity and 82% specificity when compared against experienced Endodontists.
Conclusions: OverJet AI demonstrated comparable diagnostic ability to dental professionals in detecting periapical pathology. The results suggest that AI could be a valuable tool when reading and assessing periapical radiolucencies. Further research is needed as the technologies continue to improve and develop.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
6-2-2026