Deep Learning-Based Detection and Anatomical Classification of Retained Roots and Periodontally Compromised Teeth in Orthopantomogram across Multi-National Populations

Published: 2 February 2026| Version 2 | DOI: 10.17632/y7dkrpv7g9.2
Contributors:
Zohaib Khurshid,
,
,
,
,
,
, THANTRIRA PORNTAVEETUS

Description

The purpose of this study was to develop and evaluate deep learning models for the automated detection of retained roots in panoramic radiographs, with a specific focus on comparing oriented bounding box (OBB) and axis-aligned bounding box (AABB) annotation strategies across diverse populations.A multinational dataset of 4,768 panoramic radiographs was annotated into seven clinically relevant classes using both oriented bounding boxes (OBB) and axis-aligned bounding boxes (AABB) approaches. You Only Look Once version 11 (YOLOv11) and Real-Time Detection Transformer (RT-DETR) models were trained under identical conditions and evaluated using mean average precision (mAP) and loss metrics. Inference results were further examined through qualitative case analysis to capture strengths and limitations in clinically challenging scenarios.

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Dentistry, Artificial Intelligence, Oral Surgery, Clinical Anatomy, Dental Imaging, Digital Radiography, Clinical Prosthodontics, Periodontology, Clinical Dentistry, Deep Learning

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