Thanks to the amazing work from all the volunteers, we have completed the workflow in identifying dental diseases on over 6,000 radiographs in previous workflows. It is a great milestone of the project.
We have also completed all other datasets and moved this project into the 'Finished' category. To browse other active projects that still need your classifications, check out zooniverse.org/projects.
New workflows available! Please help us label the tooth numbers and outline enamel, dentine, and pulp!
Learn moreWe need you to create bounding box annotations on teeth and identify their tooth numbers! You can start with the "Tooth Numbering Detection Examples" workflow to see how to number teeth in different example images. After you feel confident, you can start the "Tooth Numbering Detection"!
We also need your help to outline enamel, dentin, and pulp. Have a look at the "Tooth Layering Workflow".
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Dental Disease Detection Statistics
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Message from the researcher
Help us label images so we can develop an AI solution for dental disease detection
Dental Disease DetectionAbout Dental Disease Detection
We are a research team at the University of Surrey conducting research into Machine Learning solutions in the medical field. Our research for this project aims to develop an AI-assisted workflow to automatically identify multiple common dental diseases from an x-ray radiograph and generate a diagnosis report. However, to do this, we need accurately labeled radiographs.
This is where you come in! We need you to create bounding box annotations for any dental diseases you can find in a radiograph. We also need you to create annotations for tooth numbers and other tooth features.