Synthetic Orthopantomography Image Generation Using Generative Adversarial Networks for Data Augmentation.

ABSTRACT:

Objective

To overcome the scarcity of annotated dental X-ray datasets, this study presents a novel pipeline for generating high-resolution synthetic orthopantomography (OPG) images using customized generative adversarial networks (GANs).

Methods

A total of 4777 real OPG images were collected from clinical centres in Pakistan, Thailand, and the U.S., covering diverse anatomical features. Twelve GAN models were initially trained, with four top-performing variants selected for further training on both combined and region-specific datasets. Synthetic images were generated at 2048 × 1024 pixels, maintaining fine anatomical detail. The evaluation was conducted using (1) a YOLO-based object detection model trained on real OPGs to assess feature representation via mean average preci

SUBMITTER: Waqas M 

PROVIDER: S-EPMC12424425 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature