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AI-Generated Synthetic Panoramic Radiograph for Enhanced Dental Image Analysis.

Xingyue Fu1,2, Xiaoshuang Li2, Eduardo Delamare3

  • 1Biomedical Data Analysis and Visualisation (BDAV) Lab, School of Computer Science, The University of Sydney, Sydney, Australia.

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|March 10, 2026
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Summary
This summary is machine-generated.

Synthetic data generation enhances AI in medical imaging, offering privacy-preserving solutions. Integrating synthetic and real panoramic radiographs improved dental AI tasks, with high-resolution data boosting abnormality segmentation and lower-resolution data optimizing segmentation efficiency.

Keywords:
Class imbalanceConditional synthetic generationData augmentationData fusionData privacySynthetic data evaluation

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • Synthetic image data offers scalable solutions for AI in medical image analysis, addressing data scarcity, class imbalance, and privacy concerns.
  • Optimal generation and integration of synthetic and real medical data for AI applications remain under-researched.
  • Panoramic radiograph (PR) analysis in dentistry presents opportunities for AI-driven advancements.

Purpose of the Study:

  • To propose and evaluate a novel fusion framework for integrating synthetic and real data in panoramic radiograph analysis.
  • To investigate the impact of different synthetic data resolutions and fusion strategies on three dental AI tasks.
  • To assess the performance, efficiency, and privacy-preserving capabilities of synthetic-real data integration.

Main Methods:

  • Developed a clinically guided conditional generative adversarial network (GAN) to create synthetic PR datasets at 512x512 and 256x256 resolutions.
  • Evaluated four data fusion strategies (real-only, matched-distribution, class-balancing, synthetic-only) using CNN and vision foundation model (FM) pipelines.
  • Conducted experiments on three public dental datasets and performed blinded clinical evaluation of synthetic PRs.

Main Results:

  • High-resolution (512x512) synthetic data significantly improved abnormality segmentation performance.
  • Lower-resolution (256x256) synthetic data achieved comparable results for full-mouth segmentation with 40% reduced training cost.
  • Synthetic-only models maintained over 93% of real-only performance, enabling privacy-preserving AI.
  • Fine-tuning FMs with synthetic-real data fusion enhanced zero-shot abnormality segmentation by up to 17%.

Conclusions:

  • Task-aligned synthetic data generation and fusion strategies are crucial for robust medical image AI.
  • Resolution selection impacts performance and efficiency, offering trade-offs for specific dental tasks.
  • Synthetic-real data integration provides a viable, privacy-preserving approach for medical AI development with minimal performance compromise.