Artificial intelligence-powered innovations in periodontal diagnosis: a new era in dental healthcare
BackgroundThe 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...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Medical Technology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmedt.2024.1469852/full |
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| author | Jarupat Jundaeng Jarupat Jundaeng Jarupat Jundaeng Rapeeporn Chamchong Choosak Nithikathkul Choosak Nithikathkul |
| author_facet | Jarupat Jundaeng Jarupat Jundaeng Jarupat Jundaeng Rapeeporn Chamchong Choosak Nithikathkul Choosak Nithikathkul |
| author_sort | Jarupat Jundaeng |
| collection | DOAJ |
| description | BackgroundThe 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.MethodsWe 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.ResultsThe 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.ConclusionsThis 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. |
| format | Article |
| id | doaj-art-5c57e446b0cc4f1f96026ad911c27f58 |
| institution | Kabale University |
| issn | 2673-3129 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medical Technology |
| spelling | doaj-art-5c57e446b0cc4f1f96026ad911c27f582025-01-10T06:10:35ZengFrontiers Media S.A.Frontiers in Medical Technology2673-31292025-01-01610.3389/fmedt.2024.14698521469852Artificial intelligence-powered innovations in periodontal diagnosis: a new era in dental healthcareJarupat Jundaeng0Jarupat Jundaeng1Jarupat Jundaeng2Rapeeporn Chamchong3Choosak Nithikathkul4Choosak Nithikathkul5Ph.D. in Health Science Program, Faculty of Medicine, Mahasarakham University, Mahasarakham,ThailandTropical Health Innovation Research Unit, Faculty of Medicine, Mahasarakham University, Mahasarakham, ThailandDental Department, Fang Hospital, Chiang Mai, ThailandDepartment of Computer Science, Faculty of Informatics, Mahasarakham University, Mahasarakham, ThailandPh.D. in Health Science Program, Faculty of Medicine, Mahasarakham University, Mahasarakham,ThailandTropical Health Innovation Research Unit, Faculty of Medicine, Mahasarakham University, Mahasarakham, ThailandBackgroundThe 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.MethodsWe 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.ResultsThe 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.ConclusionsThis 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.https://www.frontiersin.org/articles/10.3389/fmedt.2024.1469852/fullartificial intelligenceperiodontal diseaseperiodontitis diagnosispanoramic radiographsconvolutional neural networks (CNNs) |
| spellingShingle | Jarupat Jundaeng Jarupat Jundaeng Jarupat Jundaeng Rapeeporn Chamchong Choosak Nithikathkul Choosak Nithikathkul Artificial intelligence-powered innovations in periodontal diagnosis: a new era in dental healthcare Frontiers in Medical Technology artificial intelligence periodontal disease periodontitis diagnosis panoramic radiographs convolutional neural networks (CNNs) |
| title | Artificial intelligence-powered innovations in periodontal diagnosis: a new era in dental healthcare |
| title_full | Artificial intelligence-powered innovations in periodontal diagnosis: a new era in dental healthcare |
| title_fullStr | Artificial intelligence-powered innovations in periodontal diagnosis: a new era in dental healthcare |
| title_full_unstemmed | Artificial intelligence-powered innovations in periodontal diagnosis: a new era in dental healthcare |
| title_short | Artificial intelligence-powered innovations in periodontal diagnosis: a new era in dental healthcare |
| title_sort | artificial intelligence powered innovations in periodontal diagnosis a new era in dental healthcare |
| topic | artificial intelligence periodontal disease periodontitis diagnosis panoramic radiographs convolutional neural networks (CNNs) |
| url | https://www.frontiersin.org/articles/10.3389/fmedt.2024.1469852/full |
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