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Related Experiment Video

Updated: May 12, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Mamba-Based Deep Learning Model for Automated Periapical Index Classification Using Periapical Radiographs and

Jiyun Lee1, Yudam Park2, Sunil Kim2

  • 1Department of Computer Science and Engineering, Hankuk University of Foreign Studies, Seoul, Republic of Korea.

International Endodontic Journal
|May 11, 2026
PubMed
Summary

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This summary is machine-generated.

A new AI model accurately predicts apical periodontitis (AP) using periapical radiographs (PRs) and clinical data, improving diagnosis beyond binary classifications. This Mamba-based approach enhances diagnostic consistency and supports clinical decision-making.

Area of Science:

  • Artificial Intelligence in Dentistry
  • Radiographic Interpretation
  • Diagnostic Accuracy

Background:

  • Apical periodontitis (AP) diagnosis relies on periapical radiographs (PRs) and the Periapical Index (PAI) system.
  • Current automated methods often oversimplify PAI scores or omit crucial clinical metadata, limiting diagnostic performance.
  • Inconsistent AP diagnosis complicates treatment planning and clinical decision-making.

Purpose of the Study:

  • To develop and validate a novel Mamba-based classification model for AP diagnosis.
  • To integrate periapical radiographs (PRs) with structured clinical metadata for predicting detailed PAI scores (1-5).
  • To overcome limitations of existing automated diagnostic approaches.

Main Methods:

  • A retrospective diagnostic accuracy study utilizing PRs and metadata (age, tooth location, number, arch type).
Keywords:
artificial intelligenceclassificationimage‐metadata integrationmulti‐modal learningperiapical indexperiapical periodontitisselective state space model

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Last Updated: May 12, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
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Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

  • Two endodontists assigned PAI scores (1-5), with consensus establishing the reference standard.
  • A Mamba-based AI model was developed, incorporating spatial dependencies and clinical metadata, evaluated via 5-fold cross-validation.

    • Main Results:

    • The Mamba-based model achieved 54.72% accuracy and a quadratic-weighted kappa (QWK) of 0.713 for 5-class PAI classification.
    • Performance surpassed existing Convolutional Neural Network (CNN) and object detection models.
    • Ablation studies confirmed patient age as a significant factor; Grad-CAM analysis showed clinically relevant highlighted regions.

    Conclusions:

    • The proposed model effectively utilizes the full PAI score range and integrates clinical information, addressing prior limitations.
    • It demonstrates potential for enhancing radiographic interpretation consistency and reducing inter-examiner variability.
    • The model serves as an interpretable tool for educational purposes and clinical decision support in AP diagnosis.