Accuracy of cephalometric landmark and cephalometric analysis from lateral facial photograph by using CNN-based algorithm
Abstract Cephalometric analysis is the primary diagnosis method in orthodontics. In our previous study, the algorithm was developed to estimate cephalometric landmarks from lateral facial photographs of patients with normal occlusion. This study evaluates the estimation accuracy by the algorithm tra...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82230-z |
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| author | Yui Shimamura Chie Tachiki Kaisei Takahashi Satoru Matsunaga Takashi Takaki Masafumi Hagiwara Yasushi Nishii |
| author_facet | Yui Shimamura Chie Tachiki Kaisei Takahashi Satoru Matsunaga Takashi Takaki Masafumi Hagiwara Yasushi Nishii |
| author_sort | Yui Shimamura |
| collection | DOAJ |
| description | Abstract Cephalometric analysis is the primary diagnosis method in orthodontics. In our previous study, the algorithm was developed to estimate cephalometric landmarks from lateral facial photographs of patients with normal occlusion. This study evaluates the estimation accuracy by the algorithm trained on a dataset of 2320 patients with added malocclusion patients and the analysis values. The landmarks were estimated from the input of lateral facial photographs as training data using trained CNN-based algorithms. The success detection rate (SDR) was calculated based on the mean radial error (MRE) of the distance between the estimated and actual coordinates. Furthermore, the estimated landmarks were used to measure angles and distances as a cephalometric analysis. In the skeletal Class II malocclusion, MRE was 0.42 ± 0.15 mm, and in the skeletal Class III malocclusion, MRE was 0.46 ± 0.16 mm. We conducted a cephalometric analysis using the estimated landmarks and examined the differences with actual data. In both groups, no significant differences were observed for any of the data. Our new algorithm for estimating the landmarks from lateral facial photographs of malocclusion patients resulted in an error of less than 0.5 mm; the error in cephalometric analysis was less than 0.5°. Therefore, the algorithm can be clinically valuable. |
| format | Article |
| id | doaj-art-6b94dd63b41b4fd28bd78d6078d9af4b |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-6b94dd63b41b4fd28bd78d6078d9af4b2025-08-20T02:39:34ZengNature PortfolioScientific Reports2045-23222024-12-0114111510.1038/s41598-024-82230-zAccuracy of cephalometric landmark and cephalometric analysis from lateral facial photograph by using CNN-based algorithmYui Shimamura0Chie Tachiki1Kaisei Takahashi2Satoru Matsunaga3Takashi Takaki4Masafumi Hagiwara5Yasushi Nishii6Department of Orthodontics, Tokyo Dental CollegeDepartment of Orthodontics, Tokyo Dental CollegeDepartment of Information and Computer Science, Faculty of Science and Technology, Keio UniversityOral Health Science Center, Tokyo Dental CollegeDepartment of Oral and Maxillofacial Surgery, Tokyo Dental CollegeDepartment of Information and Computer Science, Faculty of Science and Technology, Keio UniversityDepartment of Orthodontics, Tokyo Dental CollegeAbstract Cephalometric analysis is the primary diagnosis method in orthodontics. In our previous study, the algorithm was developed to estimate cephalometric landmarks from lateral facial photographs of patients with normal occlusion. This study evaluates the estimation accuracy by the algorithm trained on a dataset of 2320 patients with added malocclusion patients and the analysis values. The landmarks were estimated from the input of lateral facial photographs as training data using trained CNN-based algorithms. The success detection rate (SDR) was calculated based on the mean radial error (MRE) of the distance between the estimated and actual coordinates. Furthermore, the estimated landmarks were used to measure angles and distances as a cephalometric analysis. In the skeletal Class II malocclusion, MRE was 0.42 ± 0.15 mm, and in the skeletal Class III malocclusion, MRE was 0.46 ± 0.16 mm. We conducted a cephalometric analysis using the estimated landmarks and examined the differences with actual data. In both groups, no significant differences were observed for any of the data. Our new algorithm for estimating the landmarks from lateral facial photographs of malocclusion patients resulted in an error of less than 0.5 mm; the error in cephalometric analysis was less than 0.5°. Therefore, the algorithm can be clinically valuable.https://doi.org/10.1038/s41598-024-82230-zEstimation cephalometric landmarksLateral facial photographCNN-based algorithmCephalometric analysisMalocclusion dataCephalometric landmarks |
| spellingShingle | Yui Shimamura Chie Tachiki Kaisei Takahashi Satoru Matsunaga Takashi Takaki Masafumi Hagiwara Yasushi Nishii Accuracy of cephalometric landmark and cephalometric analysis from lateral facial photograph by using CNN-based algorithm Scientific Reports Estimation cephalometric landmarks Lateral facial photograph CNN-based algorithm Cephalometric analysis Malocclusion data Cephalometric landmarks |
| title | Accuracy of cephalometric landmark and cephalometric analysis from lateral facial photograph by using CNN-based algorithm |
| title_full | Accuracy of cephalometric landmark and cephalometric analysis from lateral facial photograph by using CNN-based algorithm |
| title_fullStr | Accuracy of cephalometric landmark and cephalometric analysis from lateral facial photograph by using CNN-based algorithm |
| title_full_unstemmed | Accuracy of cephalometric landmark and cephalometric analysis from lateral facial photograph by using CNN-based algorithm |
| title_short | Accuracy of cephalometric landmark and cephalometric analysis from lateral facial photograph by using CNN-based algorithm |
| title_sort | accuracy of cephalometric landmark and cephalometric analysis from lateral facial photograph by using cnn based algorithm |
| topic | Estimation cephalometric landmarks Lateral facial photograph CNN-based algorithm Cephalometric analysis Malocclusion data Cephalometric landmarks |
| url | https://doi.org/10.1038/s41598-024-82230-z |
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