Automated Detection of Periodontal Bone Loss Using Deep Learning and Panoramic Radiographs: A Convolutional Neural Network Approach
(1) Background: The accurate diagnosis of periodontal disease typically involves complex clinical and radiologic examination. However, recent studies have demonstrated the potential of deep learning in improving diagnostic accuracy and reliability through the development of computer-aided detection...
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/9/5261 |
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| author | Jihye Ryu Dong-Min Lee Yun-Hoa Jung OhJin Kwon SunYoung Park JaeJoon Hwang Jae-Yeol Lee |
| author_facet | Jihye Ryu Dong-Min Lee Yun-Hoa Jung OhJin Kwon SunYoung Park JaeJoon Hwang Jae-Yeol Lee |
| author_sort | Jihye Ryu |
| collection | DOAJ |
| description | (1) Background: The accurate diagnosis of periodontal disease typically involves complex clinical and radiologic examination. However, recent studies have demonstrated the potential of deep learning in improving diagnostic accuracy and reliability through the development of computer-aided detection and diagnosis algorithms for dental problems using various radiographic sources. This study focuses on the use of panoramic radiographs, which are preferred due to their ability to assess the entire dentition with a single radiation dose. The objective is to evaluate whether panoramic radiographs are a reliable source for the detection of periodontal bone loss using deep learning, and to assess its potential for practical use on a large dataset. (2) Methods: A total of 4083 anonymized digital panoramic radiographs were collected using a Proline XC machine (Planmeca Co., Helsinki, Finland) in accordance with the research ethics protocol. These images were used to train the Faster R-CNN object detection method for detecting periodontally compromised teeth on panoramic radiographs. (3) Results: This study demonstrated a high level of consistency and reproducibility among examiners, with overall inter- and intra-examiner correlation coefficient (ICC) values of 0.94. The Area Under the Curve (AUC) for detecting periodontally compromised and healthy teeth was 0.88 each, and the overall AUC for the entire jaw, including edentulous regions, was 0.91. (4) Conclusions: The regional grouping of teeth exhibited reliable detection performance for periodontal bone loss using a large dataset, indicating the possibility of automating the diagnosis of periodontitis using panoramic radiographs. |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T04:25:23Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a615bbc8504f471496c518ab404164d32023-11-17T22:31:35ZengMDPI AGApplied Sciences2076-34172023-04-01139526110.3390/app13095261Automated Detection of Periodontal Bone Loss Using Deep Learning and Panoramic Radiographs: A Convolutional Neural Network ApproachJihye Ryu0Dong-Min Lee1Yun-Hoa Jung2OhJin Kwon3SunYoung Park4JaeJoon Hwang5Jae-Yeol Lee6Department of Oral and Maxillofacial Surgery, School of Dentistry, Dental and Life Science Institute & Dental Research Institute, Pusan National University, Yangsan 50612, Republic of KoreaDepartment of Oral and Maxillofacial Surgery, School of Dentistry, Dental and Life Science Institute & Dental Research Institute, Pusan National University, Yangsan 50612, Republic of KoreaDepartment of Oral and Maxillofacial Radiology, School of Dentistry, Dental and Life Science Institute & Dental Research Institute, Pusan National University, Yangsan 50612, Republic of KoreaGlobal R & D Data Analysis Center, Data Analysis Division, Korea Institute of Science and Technology Information, Seoul 02792, Republic of KoreaBusan, Ulsan, Gyeongnam Branch, Data Analysis Division, Korea Institute of Science and Technology Information, Busan 48059, Republic of KoreaDepartment of Oral and Maxillofacial Radiology, School of Dentistry, Dental and Life Science Institute & Dental Research Institute, Pusan National University, Yangsan 50612, Republic of KoreaDepartment of Oral and Maxillofacial Surgery, School of Dentistry, Dental and Life Science Institute & Dental Research Institute, Pusan National University, Yangsan 50612, Republic of Korea(1) Background: The accurate diagnosis of periodontal disease typically involves complex clinical and radiologic examination. However, recent studies have demonstrated the potential of deep learning in improving diagnostic accuracy and reliability through the development of computer-aided detection and diagnosis algorithms for dental problems using various radiographic sources. This study focuses on the use of panoramic radiographs, which are preferred due to their ability to assess the entire dentition with a single radiation dose. The objective is to evaluate whether panoramic radiographs are a reliable source for the detection of periodontal bone loss using deep learning, and to assess its potential for practical use on a large dataset. (2) Methods: A total of 4083 anonymized digital panoramic radiographs were collected using a Proline XC machine (Planmeca Co., Helsinki, Finland) in accordance with the research ethics protocol. These images were used to train the Faster R-CNN object detection method for detecting periodontally compromised teeth on panoramic radiographs. (3) Results: This study demonstrated a high level of consistency and reproducibility among examiners, with overall inter- and intra-examiner correlation coefficient (ICC) values of 0.94. The Area Under the Curve (AUC) for detecting periodontally compromised and healthy teeth was 0.88 each, and the overall AUC for the entire jaw, including edentulous regions, was 0.91. (4) Conclusions: The regional grouping of teeth exhibited reliable detection performance for periodontal bone loss using a large dataset, indicating the possibility of automating the diagnosis of periodontitis using panoramic radiographs.https://www.mdpi.com/2076-3417/13/9/5261artificial intelligencedeep learningdiagnostic imagingperiodontitisperiodontal bone loss |
| spellingShingle | Jihye Ryu Dong-Min Lee Yun-Hoa Jung OhJin Kwon SunYoung Park JaeJoon Hwang Jae-Yeol Lee Automated Detection of Periodontal Bone Loss Using Deep Learning and Panoramic Radiographs: A Convolutional Neural Network Approach Applied Sciences artificial intelligence deep learning diagnostic imaging periodontitis periodontal bone loss |
| title | Automated Detection of Periodontal Bone Loss Using Deep Learning and Panoramic Radiographs: A Convolutional Neural Network Approach |
| title_full | Automated Detection of Periodontal Bone Loss Using Deep Learning and Panoramic Radiographs: A Convolutional Neural Network Approach |
| title_fullStr | Automated Detection of Periodontal Bone Loss Using Deep Learning and Panoramic Radiographs: A Convolutional Neural Network Approach |
| title_full_unstemmed | Automated Detection of Periodontal Bone Loss Using Deep Learning and Panoramic Radiographs: A Convolutional Neural Network Approach |
| title_short | Automated Detection of Periodontal Bone Loss Using Deep Learning and Panoramic Radiographs: A Convolutional Neural Network Approach |
| title_sort | automated detection of periodontal bone loss using deep learning and panoramic radiographs a convolutional neural network approach |
| topic | artificial intelligence deep learning diagnostic imaging periodontitis periodontal bone loss |
| url | https://www.mdpi.com/2076-3417/13/9/5261 |
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