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ORIGINAL RESEARCH article

Front. Med. Technol.
Sec. Diagnostic and Therapeutic Devices
Volume 6 - 2024 | doi: 10.3389/fmedt.2024.1469852
This article is part of the Research Topic Development of point-of-care sensors for diagnosis of bacterial-associated infections View all 3 articles

Artificial Intelligence-Powered Innovations in Periodontal Diagnosis: A New Era in Dental Healthcare

Provisionally accepted
Jarupat Jundaeng Jarupat Jundaeng 1,2Rapeeporn Chamchong Rapeeporn Chamchong 3Choosak 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, Mahasarakham, Thailand
  • 3 Department of Computer Science, Faculty of Informatics, Mahasarakham University, Kantharawichai, Thailand

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

    The aging population is increasingly affected by periodontal disease, a condition often overlooked due to its asymptomatic nature. Despite its silent onset, periodontitis is linked to various systemic conditions, contributing to severe complications and a reduced quality of life. With over a billion people globally affected, periodontal diseases present a significant public health challenge. Current diagnostic methods, including clinical exams and radiographs, have limitations, emphasizing the need for more accurate detection methods. This study aims to develop AI-driven models to enhance diagnostic precision and consistency in detecting periodontal disease. Methods: We analyzed 2,000 panoramic radiographs using image processing techniques. The YOLOv8 model segmented teeth, identified the cemento-enamel junction (CEJ), and quantified alveolar bone loss to assess stages of periodontitis. Results: The teeth segmentation model achieved an accuracy of 97%, while the CEJ and alveolar bone segmentation models reached 98%. The AI system demonstrated outstanding performance, with 94.4% accuracy and perfect sensitivity (100%), surpassing periodontists who achieved 91.1% accuracy and 90.6% sensitivity. General practitioners (GPs) benefitted from AI assistance, reaching 86.7% accuracy and 85.9% sensitivity, further improving diagnostic outcomes.This study highlights that AI models can effectively detect periodontal bone loss from panoramic radiographs, outperforming current diagnostic methods. The integration of AI into periodontal care offers faster, more accurate, and comprehensive treatment, ultimately improving patient outcomes and alleviating healthcare burdens.

    Keywords: artificial intelligence, Periodontal disease, periodontitis diagnosis, Panoramic radiographs, Convolutional Neural Networks (CNNs) 1 Introduction

    Received: 24 Jul 2024; Accepted: 23 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.