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ORIGINAL RESEARCH article

Front. Dent. Med
Sec. Periodontics
Volume 5 - 2024 | doi: 10.3389/fdmed.2024.1509361
This article is part of the Research Topic Diagnostic and Treatment Strategies for Periodontal Disease View all articles

Advanced AI-Assisted Panoramic Radiograph Analysis for Periodontal Prognostication and Alveolar Bone Loss Detection

Provisionally accepted
Jarupat Jundaeng Jarupat Jundaeng 1,2,3Rapeeporn Chamchong Rapeeporn Chamchong 4Choosak Nithikathkul Choosak Nithikathkul 1,2*
  • 1 PhD in Health Science, Faculty of Medicine, Mahasarakham University, Mahasarakham, Thailand
  • 2 Tropical Health Innovation Research Unit, Faculty of Medicine, Mahasarakham University, Kantharawichai, Thailand
  • 3 Dental Department of Fang Hospital, Chiangmai, Thailand
  • 4 Department of Computer Science, Faculty of Informatics, Mahasarakham University, Kantharawichai, Thailand

The final, formatted version of the article will be published soon.

    Background: Periodontitis is a chronic inflammatory disease affecting the gingival tissues and supporting structures of the teeth, often leading to tooth loss. The condition begins with the accumulation of dental plaque, which initiates an immune response. Current radiographic methods for assessing alveolar bone loss are subjective, timeconsuming, and labor-intensive. This study aims to develop an AI-driven model using Convolutional Neural Networks (CNNs) to accurately assess alveolar bone loss and provide individualized periodontal prognoses from panoramic radiographs. Methods: A total of 2,000 panoramic radiographs were collected using the same device, based on the periodontal diagnosis codes from the HOSxP Program. Image enhancement techniques were applied, and an AI model based on YOLOv8 was developed to segment teeth, identify the cemento-enamel junction (CEJ), and assess alveolar bone levels. The model quantified bone loss and classified prognoses for each tooth. Results: The teeth segmentation model achieved 97% accuracy, 90% sensitivity, 96% specificity, and an F1 score of 0.80. The CEJ and bone level segmentation model showed superior results with 98% accuracy, 100% sensitivity, 98% specificity, and an F1 score of 0.90. These findings confirm the models' effectiveness in analyzing panoramic radiographs for periodontal bone loss detection and prognostication. Conclusion: This AI model offers a state-of-the-art approach for assessing alveolar bone loss and predicting individualized periodontal prognoses. It provides a faster, more accurate, and less labor-intensive alternative to current methods, demonstrating its potential for improving periodontal diagnosis and patient outcomes.

    Keywords: deep learning, Convolutional neural networks (CNNs), Panoramic Radiograph Analysis, Alveolar Bone Loss Assessment, Periodontal prognosis

    Received: 14 Oct 2024; Accepted: 12 Dec 2024.

    Copyright: © 2024 Jundaeng, Chamchong and Nithikathkul. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Choosak Nithikathkul, PhD in Health Science, Faculty of Medicine, Mahasarakham University, Mahasarakham, Thailand

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.