A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs
The study aims to evaluate the diagnostic performance of an artificial intelligence system based on deep learning for the segmentation of occlusal, proximal and cervical caries lesions on panoramic radiographs. The study included 504 anonymous panoramic radiographs obtained from the radiology archiv...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/2/202 |
| _version_ | 1827626863327969280 |
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| author | Burak Dayı Hüseyin Üzen İpek Balıkçı Çiçek Şuayip Burak Duman |
| author_facet | Burak Dayı Hüseyin Üzen İpek Balıkçı Çiçek Şuayip Burak Duman |
| author_sort | Burak Dayı |
| collection | DOAJ |
| description | The study aims to evaluate the diagnostic performance of an artificial intelligence system based on deep learning for the segmentation of occlusal, proximal and cervical caries lesions on panoramic radiographs. The study included 504 anonymous panoramic radiographs obtained from the radiology archive of Inonu University Faculty of Dentistry’s Department of Oral and Maxillofacial Radiology from January 2018 to January 2020. This study proposes Dental Caries Detection Network (DCDNet) architecture for dental caries segmentation. The main difference between DCDNet and other segmentation architecture is that the last part of DCDNet contains a Multi-Predicted Output (MPO) structure. In MPO, the final feature map split into three different paths for detecting occlusal, proximal and cervical caries. Extensive experimental analyses were executed to analyze the DCDNet network architecture performance. In these comparison results, while the proposed model achieved an average F1-score of 62.79%, the highest average F1-score of 15.69% was achieved with the state-of-the-art segmentation models. These results show that the proposed artificial intelligence-based model can be one of the indispensable auxiliary tools of dentists in the diagnosis and treatment planning of carious lesions by enabling their detection in different locations with high success. |
| first_indexed | 2024-03-09T13:03:31Z |
| format | Article |
| id | doaj.art-22d94c325bf04b7189f99ef23445c295 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T13:03:31Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-22d94c325bf04b7189f99ef23445c2952023-11-30T21:51:23ZengMDPI AGDiagnostics2075-44182023-01-0113220210.3390/diagnostics13020202A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic RadiographsBurak Dayı0Hüseyin Üzen1İpek Balıkçı Çiçek2Şuayip Burak Duman3Department of Restorative Dentistry, Faculty of Dentistry, Inonu University, Malatya 44280, TurkeyDepartment of Computer Engineering, Bingol University, Bingol 12000, TurkeyDepartment of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, TurkeyDepartment of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya 44280, TurkeyThe study aims to evaluate the diagnostic performance of an artificial intelligence system based on deep learning for the segmentation of occlusal, proximal and cervical caries lesions on panoramic radiographs. The study included 504 anonymous panoramic radiographs obtained from the radiology archive of Inonu University Faculty of Dentistry’s Department of Oral and Maxillofacial Radiology from January 2018 to January 2020. This study proposes Dental Caries Detection Network (DCDNet) architecture for dental caries segmentation. The main difference between DCDNet and other segmentation architecture is that the last part of DCDNet contains a Multi-Predicted Output (MPO) structure. In MPO, the final feature map split into three different paths for detecting occlusal, proximal and cervical caries. Extensive experimental analyses were executed to analyze the DCDNet network architecture performance. In these comparison results, while the proposed model achieved an average F1-score of 62.79%, the highest average F1-score of 15.69% was achieved with the state-of-the-art segmentation models. These results show that the proposed artificial intelligence-based model can be one of the indispensable auxiliary tools of dentists in the diagnosis and treatment planning of carious lesions by enabling their detection in different locations with high success.https://www.mdpi.com/2075-4418/13/2/202caries diagnosisconvolutional neural networkdental panoramic radiographsdeep learning |
| spellingShingle | Burak Dayı Hüseyin Üzen İpek Balıkçı Çiçek Şuayip Burak Duman A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs Diagnostics caries diagnosis convolutional neural network dental panoramic radiographs deep learning |
| title | A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs |
| title_full | A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs |
| title_fullStr | A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs |
| title_full_unstemmed | A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs |
| title_short | A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs |
| title_sort | novel deep learning based approach for segmentation of different type caries lesions on panoramic radiographs |
| topic | caries diagnosis convolutional neural network dental panoramic radiographs deep learning |
| url | https://www.mdpi.com/2075-4418/13/2/202 |
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