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<p>This study develops and evaluates an automatic AI-based system for dental caries segmentation in panoramic radiographs, contributing to the expanding research on AI applications in dental imaging. Our approach implements a two-stage process: first using the object detection model for precise tooth extraction, followed by specialized segmentation techniques for caries identification. System performance evaluation yielded exceptional results, with a Dice score of 0.926, precision of 0.921, recall of 0.931, and IoU of 0.862. These metrics demonstrate the system’s high effectiveness in accurately detecting dental caries from panoramic radiographs, indicating strong potential for clinical dental diagnostics. The proposed method offers a practical approach to panoramic caries segmentation, with results highlighting both the feasibility and efficiency of AI integration in comprehensive dental diagnostics. This work demonstrates a practical implementation of a two-stage segmentation system optimized for clinical relevance.</p>

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Dual-stage deep neural network for tooth localization and caries segmentation in panoramic dental imaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月13日

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  • Sirawich Vachmanus,
  • Suchaya Pornprasertsuk-Damrongsri,
  • Pattanasak Mongkolwat,
  • Noppanan Phinklao,
  • Dhanaporn Papasratorn,
  • Jira Kitisubkanchana,
  • Sarunya Chaikantha,
  • Raweewan Arayasantiparb

摘要

This study develops and evaluates an automatic AI-based system for dental caries segmentation in panoramic radiographs, contributing to the expanding research on AI applications in dental imaging. Our approach implements a two-stage process: first using the object detection model for precise tooth extraction, followed by specialized segmentation techniques for caries identification. System performance evaluation yielded exceptional results, with a Dice score of 0.926, precision of 0.921, recall of 0.931, and IoU of 0.862. These metrics demonstrate the system’s high effectiveness in accurately detecting dental caries from panoramic radiographs, indicating strong potential for clinical dental diagnostics. The proposed method offers a practical approach to panoramic caries segmentation, with results highlighting both the feasibility and efficiency of AI integration in comprehensive dental diagnostics. This work demonstrates a practical implementation of a two-stage segmentation system optimized for clinical relevance.