Advanced AI-assisted panoramic radiograph analysis for periodontal prognostication and alveolar bone loss detection
BackgroundPeriodontitis 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 al...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Dental Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdmed.2024.1509361/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 | BackgroundPeriodontitis 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, time-consuming, 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.MethodsA 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.ResultsThe 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.ConclusionThis 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. |
| format | Article |
| id | doaj-art-6e26fa6e864b42a7a27b5b1c1d35756a |
| institution | Kabale University |
| issn | 2673-4915 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Dental Medicine |
| spelling | doaj-art-6e26fa6e864b42a7a27b5b1c1d35756a2025-01-06T06:59:17ZengFrontiers Media S.A.Frontiers in Dental Medicine2673-49152025-01-01510.3389/fdmed.2024.15093611509361Advanced AI-assisted panoramic radiograph analysis for periodontal prognostication and alveolar bone loss detectionJarupat 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, ThailandBackgroundPeriodontitis 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, time-consuming, 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.MethodsA 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.ResultsThe 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.ConclusionThis 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.https://www.frontiersin.org/articles/10.3389/fdmed.2024.1509361/fulldeep learningconvolutional neural networks (CNNs)panoramic radiograph analysisalveolar bone loss assessmentperiodontal prognosisThai Association of Periodontology |
| spellingShingle | Jarupat Jundaeng Jarupat Jundaeng Jarupat Jundaeng Rapeeporn Chamchong Choosak Nithikathkul Choosak Nithikathkul Advanced AI-assisted panoramic radiograph analysis for periodontal prognostication and alveolar bone loss detection Frontiers in Dental Medicine deep learning convolutional neural networks (CNNs) panoramic radiograph analysis alveolar bone loss assessment periodontal prognosis Thai Association of Periodontology |
| title | Advanced AI-assisted panoramic radiograph analysis for periodontal prognostication and alveolar bone loss detection |
| title_full | Advanced AI-assisted panoramic radiograph analysis for periodontal prognostication and alveolar bone loss detection |
| title_fullStr | Advanced AI-assisted panoramic radiograph analysis for periodontal prognostication and alveolar bone loss detection |
| title_full_unstemmed | Advanced AI-assisted panoramic radiograph analysis for periodontal prognostication and alveolar bone loss detection |
| title_short | Advanced AI-assisted panoramic radiograph analysis for periodontal prognostication and alveolar bone loss detection |
| title_sort | advanced ai assisted panoramic radiograph analysis for periodontal prognostication and alveolar bone loss detection |
| topic | deep learning convolutional neural networks (CNNs) panoramic radiograph analysis alveolar bone loss assessment periodontal prognosis Thai Association of Periodontology |
| url | https://www.frontiersin.org/articles/10.3389/fdmed.2024.1509361/full |
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