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X-ray Imaging01:24

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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...
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Segmented X-ray image data for diagnosing dental periapical diseases using deep learning.

Nisrean Thalji1, Emran Aljarrah2, Mohammad H Almomani3

  • 1Department of Robotics and Artificial Intelligence, Jadara University, Irbid, Jordan.

Data in Brief
|June 17, 2024
PubMed
Summary

This study introduces a new dataset of segmented dental X-rays for identifying healthy versus diseased teeth. This resource aids in developing AI tools for accurate dental pathology detection.

Keywords:
Deep learningDental periapicalDiagnosis, Detectiondental cariesperiapical pathologyx-ray data

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate differentiation between normal and abnormal dental periapical X-rays is crucial for diagnosing dental pathology.
  • Dental X-rays provide essential insights into the physiological and pathological states of teeth and surrounding tissues.
  • Existing diagnostic methods can be enhanced by automated systems leveraging medical imaging data.

Purpose of the Study:

  • To present a segmented dataset of dental periapical X-ray images.
  • To categorize images into healthy and diseased patient groups.
  • To establish a foundation for developing automated dental pathology detection systems.

Main Methods:

  • Collected 929 high-quality dental periapical X-ray images from patients at a hospital in North Jordan.
  • Employed advanced image segmentation techniques for data processing.
  • Categorized the dataset into healthy and diseased dental patient groups based on image analysis.

Main Results:

  • A labelled dataset of 929 dental periapical X-ray images was created.
  • The dataset includes diverse cases of dental diseases, bone loss, and periapical abnormalities.
  • The segmented data facilitates the development of AI models for distinguishing normal from abnormal dental conditions.

Conclusions:

  • The developed dataset is a valuable resource for training AI models in dentistry.
  • This initiative supports the creation of automated diagnostic tools for dental pathologies like caries and pulpal diseases.
  • Advancements in deep learning AI show significant promise for improving dental diagnostics and detection accuracy.