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Related Concept Videos

X-ray Imaging01:24

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...

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Related Experiment Video

Updated: Jun 12, 2026

Measuring the Complete-arch Distortion of an Optical Dental Impression
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AI Diffusion Models Generate Realistic Synthetic Dental Radiographs Using a Limited Dataset.

Brian Kirkwood1, Byeong Yeob Choi2, James Bynum3

  • 1Organ Support and Automation Technologies, U.S. Army Institute of Surgical Research, 3698 Chambers Pass, Bldg 3611, Ft. Sam Houston, San Antonio, TX 78234, USA.

Journal of Imaging
|October 28, 2025
PubMed
Summary
This summary is machine-generated.

Generative AI can create realistic synthetic dental radiographs to train Dental AI systems. Expert input and technical refinement significantly improve the quality and clinical realism of these AI-generated images.

Keywords:
artificial intelligencedata processingdeep learningdental radiographydiffusion modelhuman-in-the-loopimage generationjudgmentsynthetic data

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Dentistry

Background:

  • Limited availability of dental radiographs hinders Dental AI system development.
  • Generative AI offers a solution by creating synthetic dental radiographs (SDRs).
  • Evaluating AI-generated images requires both expert and objective assessments.

Purpose of the Study:

  • To develop clinically realistic SDRs using Generative AI.
  • To assess the impact of expert-informed data curation and model refinement on SDR quality.
  • To validate SDR realism using both subjective expert review and objective quantitative metrics.

Main Methods:

  • A stepwise approach was used to process 10,000 dental radiographs.
  • Dentist screening and selection refined training datasets for AI models.
  • Three AI models generated SDRs, which were evaluated by expert review and quantitative metrics (FID, KID).

Main Results:

  • Expert-informed curation improved SDR realism.
  • Refinement of AI model architecture further enhanced SDR quality.
  • Objective metrics (FID, KID) confirmed improvements in image fidelity due to expert input and technical refinement.

Conclusions:

  • Expert-informed data curation and domain-specific evaluation are crucial for high-fidelity SDR generation.
  • Refined AI model architectures provide a strong foundation for creating realistic SDRs.
  • The convergence of subjective and objective assessments builds confidence in the developed SDR generation methodology.