# AI Diffusion Models Generate Realistic Synthetic Dental Radiographs Using a Limited Dataset

## TL;DR

This paper shows how AI can create realistic dental X-rays using a small dataset, validated by experts and objective metrics.

## Contribution

The study introduces a refined AI model for generating synthetic dental radiographs using expert-informed curation and improved architecture.

## Key findings

- Expert-informed curation improved the realism of synthetic dental radiographs.
- Refined model architecture further enhanced image fidelity.
- FID and KID metrics confirmed the effectiveness of expert input and technical improvements.

## Abstract

Generative Artificial Intelligence (AI) has the potential to address the limited availability of dental radiographs for the development of Dental AI systems by creating clinically realistic synthetic dental radiographs (SDRs). Evaluation of artificially generated images requires both expert review and objective measures of fidelity. A stepwise approach was used to processing 10,000 dental radiographs. First, a single dentist screened images to determine if specific image selection criterion was met; this identified 225 images. From these, 200 images were randomly selected for training an AI image generation model. Second, 100 images were randomly selected from the previous training dataset and evaluated by four dentists; the expert review identified 57 images that met image selection criteria to refine training for two additional AI models. The three models were used to generate 500 SDRs each and the clinical realism of the SDRs was assessed through expert review. In addition, the SDRs generated by each model were objectively evaluated using quantitative metrics: Fréchet Inception Distance (FID) and Kernel Inception Distance (KID). Evaluation of the SDR by a dentist determined that expert-informed curation improved SDR realism, and refinement of model architecture produced further gains. FID and KID analysis confirmed that expert input and technical refinement improve image fidelity. The convergence of subjective and objective assessments strengthens confidence that the refined model architecture can serve as a foundation for SDR image generation, while highlighting the importance of expert-informed data curation and domain-specific evaluation metrics.