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An Interpretable Computer-Aided Diagnosis Method for Periodontitis From Panoramic Radiographs.

Haoyang Li1,2,3, Juexiao Zhou2,4, Yi Zhou5

  • 1Cancer Systems Biology Center, The China-Japan Union Hospital, Jilin University, Changchun, China.

Frontiers in Physiology
|July 9, 2021
PubMed
Summary

An interpretable AI method, Deetal-Perio, accurately predicts periodontitis severity from dental radiographs by analyzing alveolar bone loss (ABL). This tool aids early detection and prevents tooth loss, especially where dental professionals are scarce.

Keywords:
computer-aided diagnosticsinterpretable modelpanoramic radiographperiodontitis diagnosisteeth segmentation and numbering

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Area of Science:

  • Artificial Intelligence in Dentistry
  • Medical Image Analysis
  • Computational Periodontology

Background:

  • Periodontitis is a widespread, irreversible inflammatory disease affecting 20-50% of the global population.
  • Early detection of periodontitis is crucial for preventing tooth loss, particularly in areas with limited dental expertise.
  • Interpretable AI models are essential for clinical adoption in disease diagnosis.

Purpose of the Study:

  • To develop an interpretable computational method, Deetal-Perio, for predicting periodontitis severity using dental panoramic radiographs.
  • To establish alveolar bone loss (ABL) as a key interpretable feature for periodontitis diagnosis.
  • To improve automated screening and early detection of periodontitis.

Main Methods:

  • Deetal-Perio employs Mask R-CNN with a novel calibration for tooth segmentation and indexing.
  • It segments alveolar bone contours to calculate an alveolar bone loss (ABL) ratio for each tooth.
  • Periodontitis severity is predicted based on the ABL ratios of all teeth.

Main Results:

  • Deetal-Perio achieved high performance with Macro F1-scores of 0.894 and 0.820, and accuracies of 0.896 and 0.824 on the Suzhou and Zhongshan datasets, respectively.
  • The method demonstrated robustness and outperformed state-of-the-art approaches in periodontitis prediction and tooth segmentation.
  • The architecture provides interpretability, allowing clinicians to understand the diagnostic reasoning.

Conclusions:

  • Deetal-Perio offers an accurate and interpretable AI solution for automated periodontitis severity prediction from radiographs.
  • The method's ability to analyze alveolar bone loss (ABL) supports clinical decision-making and early intervention.
  • This tool has the potential to enhance dental care accessibility and outcomes, especially in underserved communities.