Precision Medicine Assessment of the Radiographic Defect Angle of the Intrabony Defect in Periodontal Lesions by Deep Learning of Bitewing Radiographs
In dental diagnosis, evaluating the severity of periodontal disease by analyzing the radiographic defect angle of the intrabony defect is essential for effective treatment planning. However, dentists often rely on clinical examinations and manual analysis, which can be time-consuming and labor-inten...
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2025-01-01
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| author | Patricia Angela R. Abu Yi-Cheng Mao Yuan-Jin Lin Chien-Kai Chao Yi-He Lin Bo-Siang Wang Chiung-An Chen Shih-Lun Chen Tsung-Yi Chen Kuo-Chen Li |
| author_facet | Patricia Angela R. Abu Yi-Cheng Mao Yuan-Jin Lin Chien-Kai Chao Yi-He Lin Bo-Siang Wang Chiung-An Chen Shih-Lun Chen Tsung-Yi Chen Kuo-Chen Li |
| author_sort | Patricia Angela R. Abu |
| collection | DOAJ |
| description | In dental diagnosis, evaluating the severity of periodontal disease by analyzing the radiographic defect angle of the intrabony defect is essential for effective treatment planning. However, dentists often rely on clinical examinations and manual analysis, which can be time-consuming and labor-intensive. Due to the high recurrence rate of periodontal disease after treatment, accurately evaluating the radiographic defect angle of the intrabony defect is vital for implementing targeted interventions, which can improve treatment outcomes and reduce recurrence. This study aims to streamline clinical practices and enhance patient care in managing periodontal disease by determining its severity based on the analysis of the radiographic defect angle of the intrabony defect. In this approach, radiographic defect angles of the intrabony defect greater than 37 degrees are classified as severe, while those less than 37 degrees are considered mild. This study employed a series of novel image enhancement techniques to significantly improve diagnostic accuracy. Before enhancement, the maximum accuracy was 78.85%, which increased to 95.12% following enhancement. YOLOv8 detects the affected tooth, and its mAP can reach 95.5%, with a precision reach of 94.32%. This approach assists dentists in swiftly assessing the extent of periodontal erosion, enabling timely and appropriate treatment. These techniques reduce diagnostic time and improve healthcare quality. |
| format | Article |
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| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-ce787f5c89e94b93b8cb4db3021c020d2025-01-24T13:23:04ZengMDPI AGBioengineering2306-53542025-01-011214310.3390/bioengineering12010043Precision Medicine Assessment of the Radiographic Defect Angle of the Intrabony Defect in Periodontal Lesions by Deep Learning of Bitewing RadiographsPatricia Angela R. Abu0Yi-Cheng Mao1Yuan-Jin Lin2Chien-Kai Chao3Yi-He Lin4Bo-Siang Wang5Chiung-An Chen6Shih-Lun Chen7Tsung-Yi Chen8Kuo-Chen Li9Ateneo Laboratory for Intelligent Visual Environments, Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, PhilippinesDepartment of Operative Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 33305, TaiwanDepartment of Program on Semiconductor Manufacturing Technology, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan City 701401, TaiwanDepartment of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, TaiwanDepartment of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, TaiwanDepartment of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, TaiwanDepartment of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, TaiwanDepartment of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, TaiwanDepartment of Electronic Engineering, Feng Chia University, Taichung City 40724, TaiwanDepartment of Information Management, Chung Yuan Christian University, Taoyuan City 320317, TaiwanIn dental diagnosis, evaluating the severity of periodontal disease by analyzing the radiographic defect angle of the intrabony defect is essential for effective treatment planning. However, dentists often rely on clinical examinations and manual analysis, which can be time-consuming and labor-intensive. Due to the high recurrence rate of periodontal disease after treatment, accurately evaluating the radiographic defect angle of the intrabony defect is vital for implementing targeted interventions, which can improve treatment outcomes and reduce recurrence. This study aims to streamline clinical practices and enhance patient care in managing periodontal disease by determining its severity based on the analysis of the radiographic defect angle of the intrabony defect. In this approach, radiographic defect angles of the intrabony defect greater than 37 degrees are classified as severe, while those less than 37 degrees are considered mild. This study employed a series of novel image enhancement techniques to significantly improve diagnostic accuracy. Before enhancement, the maximum accuracy was 78.85%, which increased to 95.12% following enhancement. YOLOv8 detects the affected tooth, and its mAP can reach 95.5%, with a precision reach of 94.32%. This approach assists dentists in swiftly assessing the extent of periodontal erosion, enabling timely and appropriate treatment. These techniques reduce diagnostic time and improve healthcare quality.https://www.mdpi.com/2306-5354/12/1/43convolutional neural networkimage detectionimage enhancementmachine learningradiographic defect angleintrabony defect |
| spellingShingle | Patricia Angela R. Abu Yi-Cheng Mao Yuan-Jin Lin Chien-Kai Chao Yi-He Lin Bo-Siang Wang Chiung-An Chen Shih-Lun Chen Tsung-Yi Chen Kuo-Chen Li Precision Medicine Assessment of the Radiographic Defect Angle of the Intrabony Defect in Periodontal Lesions by Deep Learning of Bitewing Radiographs Bioengineering convolutional neural network image detection image enhancement machine learning radiographic defect angle intrabony defect |
| title | Precision Medicine Assessment of the Radiographic Defect Angle of the Intrabony Defect in Periodontal Lesions by Deep Learning of Bitewing Radiographs |
| title_full | Precision Medicine Assessment of the Radiographic Defect Angle of the Intrabony Defect in Periodontal Lesions by Deep Learning of Bitewing Radiographs |
| title_fullStr | Precision Medicine Assessment of the Radiographic Defect Angle of the Intrabony Defect in Periodontal Lesions by Deep Learning of Bitewing Radiographs |
| title_full_unstemmed | Precision Medicine Assessment of the Radiographic Defect Angle of the Intrabony Defect in Periodontal Lesions by Deep Learning of Bitewing Radiographs |
| title_short | Precision Medicine Assessment of the Radiographic Defect Angle of the Intrabony Defect in Periodontal Lesions by Deep Learning of Bitewing Radiographs |
| title_sort | precision medicine assessment of the radiographic defect angle of the intrabony defect in periodontal lesions by deep learning of bitewing radiographs |
| topic | convolutional neural network image detection image enhancement machine learning radiographic defect angle intrabony defect |
| url | https://www.mdpi.com/2306-5354/12/1/43 |
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