Controllable Panoramic Radiograph Synthesis Using a Generative Model

Panoramic radiography (PR) is the one of the most widely prescribed diagnostic imaging modalities in dentistry. Achieving clinical-level automated interpretation of PR is critical for improving diagnostic efficiency, reducing radiologist workload, and ensuring interpretive consistency. Artificial intelligence (AI) has demonstrated significant utility and promise in PR interpretation, improving diagnostic efficiency, reducing radiologists' workload, and enhancing interpretive consistency. However, developing AI-based PR interpretation methods remains hindered by persistent challenges of data scarcity, privacy constraints, and annotation imbalance. Facing these limitations, the aim of this study is to develop a disease-guided, anatomy-controllable generative model, named PRGen (for "panoramic radiograph generation"), designed to mitigate challenges related to data scarcity, privacy constraints, and annotation imbalance. PRGen is developed using 50,127 paired text-image samples and overcomes the uncontrollability of existing generative models, synthesizing both realistic PRs and paired masks from textual disease descriptions, enabling precise control of dental anatomy through simple or detailed sketches, and effectively mitigating barriers to large-scale, well-annotated dataset construction. We comprehensively evaluated the quality and clinical utility of PRGen-generated PRs across held-out internal test sets, publicly available datasets, and assessments by 10 radiologists. Incorporating PRGen-synthesized images into training led to a 47.59% improvement in Dice score for segmentation and a 11.53% increase in the area under the curve. Upon expert review, more than 82% of synthesized PRs were judged to faithfully reflect the described diseases while maintaining clinically realistic anatomical structures and radiographic appearances. External multicenter validation confirmed robust generalizability, with an average Dice improvement of 25.58% for segmentation and consistent gains in diagnostic tasks. Collectively, these results demonstrate that PRGen enables the generation of high-quality, mask-annotated PRs, thereby reducing the reliance on manual annotations and supporting more reliable and automated analysis of PRs, ultimately facilitating both model development and clinical translation.