Clinical-oriented 3D visualization and quantitative analysis of gingival thickness using convolutional neural networks and CBCT
ObjectiveTraditional gingival thickness (GT) assessment methods provide only point measurements or simple classifications, lacking spatial distribution information. This study aimed to develop a CBCT-based 3D visualization system for gingival thickness using deep learning, providing a novel spatial...
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| Format: | Article |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Dental Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdmed.2025.1635155/full |
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| author | Lan Yang Lan Yang ZiCheng Zhu Yongshan Li Jieying Huang Xiaoli Wang Haoran Zheng Jiang Chen |
| author_facet | Lan Yang Lan Yang ZiCheng Zhu Yongshan Li Jieying Huang Xiaoli Wang Haoran Zheng Jiang Chen |
| author_sort | Lan Yang |
| collection | DOAJ |
| description | ObjectiveTraditional gingival thickness (GT) assessment methods provide only point measurements or simple classifications, lacking spatial distribution information. This study aimed to develop a CBCT-based 3D visualization system for gingival thickness using deep learning, providing a novel spatial assessment tool for implant surgery planning.MethodsCBCT and intraoral scanning (IOS) data from 50 patients with tooth loss were collected to establish a standardized dataset. DeepLabV3+ architecture was employed for semantic segmentation of gingival and bone tissues. A 3D visualization algorithm incorporating vertical scanning strategy, triangular mesh construction, and gradient color mapping was innovatively developed to transform 2D slices into continuous 3D surfaces.ResultsThe semantic segmentation model achieved a mIoU of 85.92 ± 0.43%. The 3D visualization system successfully constructed a comprehensive spatial distribution model of gingival thickness, clearly demonstrating GT variations from alveolar ridge to labial aspect through gradient coloration. The 3D model enabled millimeter-precision quantification, supporting multi-angle and multi-level GT assessment that overcame the limitations of traditional 2D measurements.ConclusionThis system represents a methodological advancement from qualitative to spatial quantitative GT assessment. The intuitive 3D visualization serves as an innovative preoperative tool that identifies high-risk areas and guides personalized surgical planning, enhancing predictability for aesthetic and complex implant cases. |
| format | Article |
| id | doaj-art-e2c2d34ede2c4f1995baa9311289b514 |
| institution | Kabale University |
| issn | 2673-4915 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Dental Medicine |
| spelling | doaj-art-e2c2d34ede2c4f1995baa9311289b5142025-08-20T04:02:51ZengFrontiers Media S.A.Frontiers in Dental Medicine2673-49152025-08-01610.3389/fdmed.2025.16351551635155Clinical-oriented 3D visualization and quantitative analysis of gingival thickness using convolutional neural networks and CBCTLan Yang0Lan Yang1ZiCheng Zhu2Yongshan Li3Jieying Huang4Xiaoli Wang5Haoran Zheng6Jiang Chen7School of Stomatology, Craniomaxillofacial Implant Research Center, Fujian Medical University, Fuzhou, Fujian, ChinaGuangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Department of Oral Implantology, School and Hospital of Stomatology, Guangzhou Medical University, Guangzhou, Guangdong, ChinaSchool of Data Science & School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, ChinaGuangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Department of Oral Implantology, School and Hospital of Stomatology, Guangzhou Medical University, Guangzhou, Guangdong, ChinaGuangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Department of Oral Implantology, School and Hospital of Stomatology, Guangzhou Medical University, Guangzhou, Guangdong, ChinaSchool of Intelligent Vehicles, Guangzhou Panyu Polytechnic, Guangzhou, ChinaSchool of Intelligent Vehicles, Guangzhou Panyu Polytechnic, Guangzhou, ChinaSchool of Stomatology, Craniomaxillofacial Implant Research Center, Fujian Medical University, Fuzhou, Fujian, ChinaObjectiveTraditional gingival thickness (GT) assessment methods provide only point measurements or simple classifications, lacking spatial distribution information. This study aimed to develop a CBCT-based 3D visualization system for gingival thickness using deep learning, providing a novel spatial assessment tool for implant surgery planning.MethodsCBCT and intraoral scanning (IOS) data from 50 patients with tooth loss were collected to establish a standardized dataset. DeepLabV3+ architecture was employed for semantic segmentation of gingival and bone tissues. A 3D visualization algorithm incorporating vertical scanning strategy, triangular mesh construction, and gradient color mapping was innovatively developed to transform 2D slices into continuous 3D surfaces.ResultsThe semantic segmentation model achieved a mIoU of 85.92 ± 0.43%. The 3D visualization system successfully constructed a comprehensive spatial distribution model of gingival thickness, clearly demonstrating GT variations from alveolar ridge to labial aspect through gradient coloration. The 3D model enabled millimeter-precision quantification, supporting multi-angle and multi-level GT assessment that overcame the limitations of traditional 2D measurements.ConclusionThis system represents a methodological advancement from qualitative to spatial quantitative GT assessment. The intuitive 3D visualization serves as an innovative preoperative tool that identifies high-risk areas and guides personalized surgical planning, enhancing predictability for aesthetic and complex implant cases.https://www.frontiersin.org/articles/10.3389/fdmed.2025.1635155/full3D visualizationgingival thicknessdeep learningcone beam computed tomographyimplant restoration |
| spellingShingle | Lan Yang Lan Yang ZiCheng Zhu Yongshan Li Jieying Huang Xiaoli Wang Haoran Zheng Jiang Chen Clinical-oriented 3D visualization and quantitative analysis of gingival thickness using convolutional neural networks and CBCT Frontiers in Dental Medicine 3D visualization gingival thickness deep learning cone beam computed tomography implant restoration |
| title | Clinical-oriented 3D visualization and quantitative analysis of gingival thickness using convolutional neural networks and CBCT |
| title_full | Clinical-oriented 3D visualization and quantitative analysis of gingival thickness using convolutional neural networks and CBCT |
| title_fullStr | Clinical-oriented 3D visualization and quantitative analysis of gingival thickness using convolutional neural networks and CBCT |
| title_full_unstemmed | Clinical-oriented 3D visualization and quantitative analysis of gingival thickness using convolutional neural networks and CBCT |
| title_short | Clinical-oriented 3D visualization and quantitative analysis of gingival thickness using convolutional neural networks and CBCT |
| title_sort | clinical oriented 3d visualization and quantitative analysis of gingival thickness using convolutional neural networks and cbct |
| topic | 3D visualization gingival thickness deep learning cone beam computed tomography implant restoration |
| url | https://www.frontiersin.org/articles/10.3389/fdmed.2025.1635155/full |
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