An Efficient Hybrid 3D Computer-Aided Cephalometric Analysis for Lateral Cephalometric and Cone-Beam Computed Tomography (CBCT) Systems
Lateral cephalometric analysis is commonly used in orthodontics for skeletal classification to ensure an accurate and reliable diagnosis for treatment planning. However, most current research depends on analyzing different type of radiographs, which requires more computational time than 3D analysis....
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2025-06-01
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| author | Laurine A. Ashame Sherin M. Youssef Mazen Nabil Elagamy Sahar M. El-Sheikh |
| author_facet | Laurine A. Ashame Sherin M. Youssef Mazen Nabil Elagamy Sahar M. El-Sheikh |
| author_sort | Laurine A. Ashame |
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| description | Lateral cephalometric analysis is commonly used in orthodontics for skeletal classification to ensure an accurate and reliable diagnosis for treatment planning. However, most current research depends on analyzing different type of radiographs, which requires more computational time than 3D analysis. Consequently, this study addresses fully automatic orthodontics tracing based on the usage of artificial intelligence (AI) applied to 2D and 3D images, by designing a cephalometric system that analyzes the significant landmarks and regions of interest (ROI) needed in orthodontics tracing, especially for the mandible and maxilla teeth. In this research, a computerized system is developed to automate the tasks of orthodontics evaluation during 2D and Cone-Beam Computed Tomography (CBCT or 3D) systems measurements. This work was tested on a dataset that contains images of males and females obtained from dental hospitals with patient-informed consent. The dataset consists of 2D lateral cephalometric, panorama and CBCT radiographs. Many scenarios were applied to test the proposed system in landmark prediction and detection. Moreover, this study integrates the Grad-CAM (Gradient-Weighted Class Activation Mapping) technique to generate heat maps, providing transparent visualization of the regions the model focuses on during its decision-making process. By enhancing the interpretability of deep learning predictions, Grad-CAM strengthens clinical confidence in the system’s outputs, ensuring that ROI detection aligns with orthodontic diagnostic standards. This explainability is crucial in medical AI applications, where understanding model behavior is as important as achieving high accuracy. The experimental results achieved an accuracy exceeding 98.9%. This research evaluates and differentiates between the two-dimensional and the three-dimensional tracing analyses applied to measurements based on the practices of the European Board of Orthodontics. The results demonstrate the proposed methodology’s robustness when applied to cephalometric images. Furthermore, the evaluation of 3D analysis usage provides a clear understanding of the significance of integrated deep-learning techniques in orthodontics. |
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| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-06-01 |
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| series | Computers |
| spelling | doaj-art-b69635e4a14e4473aff4a40fbd378ce52025-08-20T03:27:30ZengMDPI AGComputers2073-431X2025-06-0114622310.3390/computers14060223An Efficient Hybrid 3D Computer-Aided Cephalometric Analysis for Lateral Cephalometric and Cone-Beam Computed Tomography (CBCT) SystemsLaurine A. Ashame0Sherin M. Youssef1Mazen Nabil Elagamy2Sahar M. El-Sheikh3Computer Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Abu Qir, Alexandria 1029, EgyptComputer Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Abu Qir, Alexandria 1029, EgyptComputer Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Abu Qir, Alexandria 1029, EgyptDepartment of Oral Pathology, Faculty of Dentistry, Alexandria University, Alexandria 21521, EgyptLateral cephalometric analysis is commonly used in orthodontics for skeletal classification to ensure an accurate and reliable diagnosis for treatment planning. However, most current research depends on analyzing different type of radiographs, which requires more computational time than 3D analysis. Consequently, this study addresses fully automatic orthodontics tracing based on the usage of artificial intelligence (AI) applied to 2D and 3D images, by designing a cephalometric system that analyzes the significant landmarks and regions of interest (ROI) needed in orthodontics tracing, especially for the mandible and maxilla teeth. In this research, a computerized system is developed to automate the tasks of orthodontics evaluation during 2D and Cone-Beam Computed Tomography (CBCT or 3D) systems measurements. This work was tested on a dataset that contains images of males and females obtained from dental hospitals with patient-informed consent. The dataset consists of 2D lateral cephalometric, panorama and CBCT radiographs. Many scenarios were applied to test the proposed system in landmark prediction and detection. Moreover, this study integrates the Grad-CAM (Gradient-Weighted Class Activation Mapping) technique to generate heat maps, providing transparent visualization of the regions the model focuses on during its decision-making process. By enhancing the interpretability of deep learning predictions, Grad-CAM strengthens clinical confidence in the system’s outputs, ensuring that ROI detection aligns with orthodontic diagnostic standards. This explainability is crucial in medical AI applications, where understanding model behavior is as important as achieving high accuracy. The experimental results achieved an accuracy exceeding 98.9%. This research evaluates and differentiates between the two-dimensional and the three-dimensional tracing analyses applied to measurements based on the practices of the European Board of Orthodontics. The results demonstrate the proposed methodology’s robustness when applied to cephalometric images. Furthermore, the evaluation of 3D analysis usage provides a clear understanding of the significance of integrated deep-learning techniques in orthodontics.https://www.mdpi.com/2073-431X/14/6/223AIlateral cephalometricCBCTdeep learninghandcrafted featuressegmentation |
| spellingShingle | Laurine A. Ashame Sherin M. Youssef Mazen Nabil Elagamy Sahar M. El-Sheikh An Efficient Hybrid 3D Computer-Aided Cephalometric Analysis for Lateral Cephalometric and Cone-Beam Computed Tomography (CBCT) Systems Computers AI lateral cephalometric CBCT deep learning handcrafted features segmentation |
| title | An Efficient Hybrid 3D Computer-Aided Cephalometric Analysis for Lateral Cephalometric and Cone-Beam Computed Tomography (CBCT) Systems |
| title_full | An Efficient Hybrid 3D Computer-Aided Cephalometric Analysis for Lateral Cephalometric and Cone-Beam Computed Tomography (CBCT) Systems |
| title_fullStr | An Efficient Hybrid 3D Computer-Aided Cephalometric Analysis for Lateral Cephalometric and Cone-Beam Computed Tomography (CBCT) Systems |
| title_full_unstemmed | An Efficient Hybrid 3D Computer-Aided Cephalometric Analysis for Lateral Cephalometric and Cone-Beam Computed Tomography (CBCT) Systems |
| title_short | An Efficient Hybrid 3D Computer-Aided Cephalometric Analysis for Lateral Cephalometric and Cone-Beam Computed Tomography (CBCT) Systems |
| title_sort | efficient hybrid 3d computer aided cephalometric analysis for lateral cephalometric and cone beam computed tomography cbct systems |
| topic | AI lateral cephalometric CBCT deep learning handcrafted features segmentation |
| url | https://www.mdpi.com/2073-431X/14/6/223 |
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