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Getting to the Root of Dental Defect Detection With Synthetic Data and Machine Learning

Dental radiograph examination can be a prolonged and inexact science for providers. To make it a faster, more efficient process, our team developed a proof of concept using machine learning and synthetic data principles.


Machine learning (ML) and synthetic data are primarily associated with automation and other next-generation bespoke solutions, but there’s value in integrating them into other industries and processes. To illustrate the breadth of synthetic and ML use cases, our team developed a proof of concept to demonstrate how these solutions could help insurers and dental providers analyse radiographs more effectively and efficiently. 


Providing Clarity into Ongoing Issues 


For almost 130 years, X-ray technology has been informing healthcare diagnostics and decision-making. But it’s not without struggle for insurers that collaborate with dental health providers. Those issues typically boil down to: 

■ Radiograph inconsistency: The quality and consistency of dental radiographs vary, complicating the claims approval process.

■ Significant time investments: Due to the high volume of imagery, assessing radiographs and processing claims becomes time intensive.

  • Required expertise: The necessity for expert evaluations of each claim introduces potential bottlenecks, from processing delays to scaling challenges.


  • Understanding the universality of these issues, we worked with Dr. Paul R. Amato, DDS, FAGD to conceptualise a radiograph assessment algorithm. Dr. Amato collaborated with us to distil his team’s knowledge down into repeatable, scalable steps. With his domain expertise, our goal was to empower synthetic-data-driven ML to act and streamline the approval process for patient diagnosis and treatment plans. 

  • Solving Operational Logjams 

  • In this proof of concept, we focused on bitewing (both levels of teeth on one side of the mouth) and periapical (a root-to-crown image) radiographs. We crafted synthetic representations of these views by:

dental 1 dental 2 dental 3

  • Accelerating Solutions 

  • In a matter of two weeks, our model improved and was able to accurately identify individual teeth and specific conditions. In this process we obtained insight into the benefits of machine learning and synthetic data for in the dental field. 

    Enhanced Diagnostic Models: Boost the accuracy and dependability of dental diagnostic models by training them on diverse and lifelike synthetic X-ray datasets.

  • Optimized Radiographic Imagery: Fine-tune dental radiographic visuals under varied conditions, ensuring clarity and precision for dental practitioners and insurers.

■ Radiograph Privacy and Compliance: Rely on realistic, non-sensitive synthetic X-ray data to maintain patient privacy and align with data protection regulations.

  • Unlock Advanced Dental Analysis Features: Detailed annotation capabilities made possible by synthetic data make the development of cutting-edge diagnostic features possible.
  • Accelerated Radiographic Innovations: Rapid develop and fine-tuning of datasets and machine learning features eliminates the hurdles of traditional data collection and annotation.
  • This demonstration provided a glimpse into how ML and synthetic can combine their unique abilities to create innovative solutions in a challenging domain. Furthermore, it acted as a preview of the broad range of opportunities visual synthetic data can provide in the healthcare and insurance industries. 


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For a more in-depth discussion about our work with Amato Dental, watch our presentation on it from SIGGRAPH2023 here.