Publications
2025
Roseth, Jeffrey; Kim, Jong-Hak; Moon, Jun-Ho; Ko, Dong-Yub; Oh, Heesoo; Lee, Shin-Jae; Suh, Heeyeon
In: The Angle Orthodontist, vol. 95, iss. 3, pp. 249-258, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, Growth prediction, longitudinal craniofacial growth records
@article{Roseth2025,
title = {Comparison of individualized facial growth prediction models using artificial intelligence and partial least squares based on the Mathews growth collection},
author = {Jeffrey Roseth and Jong-Hak Kim and Jun-Ho Moon and Dong-Yub Ko and Heesoo Oh and Shin-Jae Lee and Heeyeon Suh},
url = {https://angle-orthodontist.kglmeridian.com/view/journals/angl/95/3/article-p249.xml},
doi = {10.2319/082124-687.1},
year = {2025},
date = {2025-01-31},
journal = {The Angle Orthodontist},
volume = {95},
issue = {3},
pages = {249-258},
abstract = {Objectives: To develop facial growth prediction models using artificial intelligence (AI) under various conditions, and to compare performance of these models with each other as well as with the partial least squares (PLS) growth prediction model. Materials and Methods: Longitudinal lateral cephalograms from 33 subjects in the Mathews growth collection were utilized. A total of 1257 pairs of before and after growth lateral cephalograms were included. In each image, 46 hard and 32 soft tissue landmarks were manually identified. Growth prediction models were constructed using a deep learning method based on TabNet deep neural network and partial least squares (PLS) method. Prediction accuracies of the two methods were compared. Results: On average, artificial intelligence (AI) showed 0.61 mm less prediction error than PLS. Among the 77 predicted landmarks, AI was more accurate than PLS in 60 landmarks. When comparing AI models with varying numbers of training epochs, those with higher epochs yielded more accurate predictions. Overall, PLS and AI exhibited greater prediction errors for soft tissue and mandibular landmarks compared to hard tissue and maxillary landmarks. However, AI showed a smaller increase in prediction error in areas with greater variability. Conclusions: AI proved to be a valuable growth prediction method, with clinically acceptable prediction errors averaging 1.49 mm for 45 hard tissue landmarks and 1.71 mm for 32 soft tissue landmarks. PLS accurately predicted landmarks with low variability. However, AI generally outperformed PLS, particularly for landmarks in the lower part of the craniofacial structure and soft tissue, where uncertainty is considerable. (Angle Orthod. 2025;95:249–258.)},
keywords = {artificial intelligence, Growth prediction, longitudinal craniofacial growth records},
pubstate = {published},
tppubtype = {article}
}
Objectives: To develop facial growth prediction models using artificial intelligence (AI) under various conditions, and to compare performance of these models with each other as well as with the partial least squares (PLS) growth prediction model. Materials and Methods: Longitudinal lateral cephalograms from 33 subjects in the Mathews growth collection were utilized. A total of 1257 pairs of before and after growth lateral cephalograms were included. In each image, 46 hard and 32 soft tissue landmarks were manually identified. Growth prediction models were constructed using a deep learning method based on TabNet deep neural network and partial least squares (PLS) method. Prediction accuracies of the two methods were compared. Results: On average, artificial intelligence (AI) showed 0.61 mm less prediction error than PLS. Among the 77 predicted landmarks, AI was more accurate than PLS in 60 landmarks. When comparing AI models with varying numbers of training epochs, those with higher epochs yielded more accurate predictions. Overall, PLS and AI exhibited greater prediction errors for soft tissue and mandibular landmarks compared to hard tissue and maxillary landmarks. However, AI showed a smaller increase in prediction error in areas with greater variability. Conclusions: AI proved to be a valuable growth prediction method, with clinically acceptable prediction errors averaging 1.49 mm for 45 hard tissue landmarks and 1.71 mm for 32 soft tissue landmarks. PLS accurately predicted landmarks with low variability. However, AI generally outperformed PLS, particularly for landmarks in the lower part of the craniofacial structure and soft tissue, where uncertainty is considerable. (Angle Orthod. 2025;95:249–258.)
Kim, Jong-Hak; Moon, Jun-Ho; Roseth, Jeffrey; Suh, Heeyeon; Oh, Heesoo; Lee, Shin-Jae
Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children Journal Article
In: The Angle Orthodontist, vol. 95, iss. 2, pp. 219-226, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, Growth prediction, Longitudinal studies, machine learning
@article{Kim2025,
title = {Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children},
author = {Jong-Hak Kim and Jun-Ho Moon and Jeffrey Roseth and Heeyeon Suh and Heesoo Oh and Shin-Jae Lee},
url = {https://angle-orthodontist.kglmeridian.com/view/journals/angl/95/2/article-p219.xml},
doi = {10.2319/052324-399.1},
year = {2025},
date = {2025-01-13},
journal = {The Angle Orthodontist},
volume = {95},
issue = {2},
pages = {219-226},
abstract = {Objectives: To compare differences in craniofacial growth prediction results for Korean and American children according to growth prediction models developed using Korean and American longitudinal growth data. Materials and Methods: Growth prediction models based on cephalometric landmarks were built for each population using longitudinally taken lateral cephalograms of Korean children and American children of northern European origin. The sample sizes of the serial datasets were 679 and 1257 for Korean and American children, respectively. On each cephalogram, 78 cephalometric landmarks were identified. The prediction models were based on the partial least squares method with 160 input and 154 output variables. For each group, growth was predicted by applying the prediction models developed using data from the same and different populations. The growth prediction results were compared and analyzed. Results: The growth prediction results obtained with the prediction model developed using data from the same population were more accurate (P<.0001). The results distinctively visualized the discrepancies in the growth prediction results if different population types were not considered. Conclusions: Applying a growth prediction model generated using data from the same population may be desirable. (Angle Orthod. 2025;95:219–226.)},
keywords = {artificial intelligence, Growth prediction, Longitudinal studies, machine learning},
pubstate = {published},
tppubtype = {article}
}
Objectives: To compare differences in craniofacial growth prediction results for Korean and American children according to growth prediction models developed using Korean and American longitudinal growth data. Materials and Methods: Growth prediction models based on cephalometric landmarks were built for each population using longitudinally taken lateral cephalograms of Korean children and American children of northern European origin. The sample sizes of the serial datasets were 679 and 1257 for Korean and American children, respectively. On each cephalogram, 78 cephalometric landmarks were identified. The prediction models were based on the partial least squares method with 160 input and 154 output variables. For each group, growth was predicted by applying the prediction models developed using data from the same and different populations. The growth prediction results were compared and analyzed. Results: The growth prediction results obtained with the prediction model developed using data from the same population were more accurate (P<.0001). The results distinctively visualized the discrepancies in the growth prediction results if different population types were not considered. Conclusions: Applying a growth prediction model generated using data from the same population may be desirable. (Angle Orthod. 2025;95:219–226.)
Roseth, Jeffrey; Kim, Jong-Hak; Moon, Jun-Ho; Ko, Dong-Yub; Oh, Heesoo; Lee, Shin-Jae; Suh, Heeyeon
Comparison of individualized facial growth prediction models using artificial intelligence and partial least squares based on the Mathews growth collection Journal Article
In: The Angle Orthodontist, vol. 95, iss. 3, pp. 249-258, 2025.
@article{Roseth2025,
title = {Comparison of individualized facial growth prediction models using artificial intelligence and partial least squares based on the Mathews growth collection},
author = {Jeffrey Roseth and Jong-Hak Kim and Jun-Ho Moon and Dong-Yub Ko and Heesoo Oh and Shin-Jae Lee and Heeyeon Suh},
url = {https://angle-orthodontist.kglmeridian.com/view/journals/angl/95/3/article-p249.xml},
doi = {10.2319/082124-687.1},
year = {2025},
date = {2025-01-31},
journal = {The Angle Orthodontist},
volume = {95},
issue = {3},
pages = {249-258},
abstract = {Objectives: To develop facial growth prediction models using artificial intelligence (AI) under various conditions, and to compare performance of these models with each other as well as with the partial least squares (PLS) growth prediction model. Materials and Methods: Longitudinal lateral cephalograms from 33 subjects in the Mathews growth collection were utilized. A total of 1257 pairs of before and after growth lateral cephalograms were included. In each image, 46 hard and 32 soft tissue landmarks were manually identified. Growth prediction models were constructed using a deep learning method based on TabNet deep neural network and partial least squares (PLS) method. Prediction accuracies of the two methods were compared. Results: On average, artificial intelligence (AI) showed 0.61 mm less prediction error than PLS. Among the 77 predicted landmarks, AI was more accurate than PLS in 60 landmarks. When comparing AI models with varying numbers of training epochs, those with higher epochs yielded more accurate predictions. Overall, PLS and AI exhibited greater prediction errors for soft tissue and mandibular landmarks compared to hard tissue and maxillary landmarks. However, AI showed a smaller increase in prediction error in areas with greater variability. Conclusions: AI proved to be a valuable growth prediction method, with clinically acceptable prediction errors averaging 1.49 mm for 45 hard tissue landmarks and 1.71 mm for 32 soft tissue landmarks. PLS accurately predicted landmarks with low variability. However, AI generally outperformed PLS, particularly for landmarks in the lower part of the craniofacial structure and soft tissue, where uncertainty is considerable. (Angle Orthod. 2025;95:249–258.)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objectives: To develop facial growth prediction models using artificial intelligence (AI) under various conditions, and to compare performance of these models with each other as well as with the partial least squares (PLS) growth prediction model. Materials and Methods: Longitudinal lateral cephalograms from 33 subjects in the Mathews growth collection were utilized. A total of 1257 pairs of before and after growth lateral cephalograms were included. In each image, 46 hard and 32 soft tissue landmarks were manually identified. Growth prediction models were constructed using a deep learning method based on TabNet deep neural network and partial least squares (PLS) method. Prediction accuracies of the two methods were compared. Results: On average, artificial intelligence (AI) showed 0.61 mm less prediction error than PLS. Among the 77 predicted landmarks, AI was more accurate than PLS in 60 landmarks. When comparing AI models with varying numbers of training epochs, those with higher epochs yielded more accurate predictions. Overall, PLS and AI exhibited greater prediction errors for soft tissue and mandibular landmarks compared to hard tissue and maxillary landmarks. However, AI showed a smaller increase in prediction error in areas with greater variability. Conclusions: AI proved to be a valuable growth prediction method, with clinically acceptable prediction errors averaging 1.49 mm for 45 hard tissue landmarks and 1.71 mm for 32 soft tissue landmarks. PLS accurately predicted landmarks with low variability. However, AI generally outperformed PLS, particularly for landmarks in the lower part of the craniofacial structure and soft tissue, where uncertainty is considerable. (Angle Orthod. 2025;95:249–258.)
Kim, Jong-Hak; Moon, Jun-Ho; Roseth, Jeffrey; Suh, Heeyeon; Oh, Heesoo; Lee, Shin-Jae
Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children Journal Article
In: The Angle Orthodontist, vol. 95, iss. 2, pp. 219-226, 2025.
@article{Kim2025,
title = {Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children},
author = {Jong-Hak Kim and Jun-Ho Moon and Jeffrey Roseth and Heeyeon Suh and Heesoo Oh and Shin-Jae Lee},
url = {https://angle-orthodontist.kglmeridian.com/view/journals/angl/95/2/article-p219.xml},
doi = {10.2319/052324-399.1},
year = {2025},
date = {2025-01-13},
journal = {The Angle Orthodontist},
volume = {95},
issue = {2},
pages = {219-226},
abstract = {Objectives: To compare differences in craniofacial growth prediction results for Korean and American children according to growth prediction models developed using Korean and American longitudinal growth data. Materials and Methods: Growth prediction models based on cephalometric landmarks were built for each population using longitudinally taken lateral cephalograms of Korean children and American children of northern European origin. The sample sizes of the serial datasets were 679 and 1257 for Korean and American children, respectively. On each cephalogram, 78 cephalometric landmarks were identified. The prediction models were based on the partial least squares method with 160 input and 154 output variables. For each group, growth was predicted by applying the prediction models developed using data from the same and different populations. The growth prediction results were compared and analyzed. Results: The growth prediction results obtained with the prediction model developed using data from the same population were more accurate (P<.0001). The results distinctively visualized the discrepancies in the growth prediction results if different population types were not considered. Conclusions: Applying a growth prediction model generated using data from the same population may be desirable. (Angle Orthod. 2025;95:219–226.)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objectives: To compare differences in craniofacial growth prediction results for Korean and American children according to growth prediction models developed using Korean and American longitudinal growth data. Materials and Methods: Growth prediction models based on cephalometric landmarks were built for each population using longitudinally taken lateral cephalograms of Korean children and American children of northern European origin. The sample sizes of the serial datasets were 679 and 1257 for Korean and American children, respectively. On each cephalogram, 78 cephalometric landmarks were identified. The prediction models were based on the partial least squares method with 160 input and 154 output variables. For each group, growth was predicted by applying the prediction models developed using data from the same and different populations. The growth prediction results were compared and analyzed. Results: The growth prediction results obtained with the prediction model developed using data from the same population were more accurate (P<.0001). The results distinctively visualized the discrepancies in the growth prediction results if different population types were not considered. Conclusions: Applying a growth prediction model generated using data from the same population may be desirable. (Angle Orthod. 2025;95:219–226.)
2025 |
Roseth, Jeffrey; Kim, Jong-Hak; Moon, Jun-Ho; Ko, Dong-Yub; Oh, Heesoo; Lee, Shin-Jae; Suh, Heeyeon: Comparison of individualized facial growth prediction models using artificial intelligence and partial least squares based on the Mathews growth collection. In: The Angle Orthodontist, vol. 95, iss. 3, pp. 249-258, 2025. (Type: Journal Article | Abstract | Links | BibTeX | Tags: artificial intelligence, Growth prediction, longitudinal craniofacial growth records)@article{Roseth2025,Objectives: To develop facial growth prediction models using artificial intelligence (AI) under various conditions, and to compare performance of these models with each other as well as with the partial least squares (PLS) growth prediction model. Materials and Methods: Longitudinal lateral cephalograms from 33 subjects in the Mathews growth collection were utilized. A total of 1257 pairs of before and after growth lateral cephalograms were included. In each image, 46 hard and 32 soft tissue landmarks were manually identified. Growth prediction models were constructed using a deep learning method based on TabNet deep neural network and partial least squares (PLS) method. Prediction accuracies of the two methods were compared. Results: On average, artificial intelligence (AI) showed 0.61 mm less prediction error than PLS. Among the 77 predicted landmarks, AI was more accurate than PLS in 60 landmarks. When comparing AI models with varying numbers of training epochs, those with higher epochs yielded more accurate predictions. Overall, PLS and AI exhibited greater prediction errors for soft tissue and mandibular landmarks compared to hard tissue and maxillary landmarks. However, AI showed a smaller increase in prediction error in areas with greater variability. Conclusions: AI proved to be a valuable growth prediction method, with clinically acceptable prediction errors averaging 1.49 mm for 45 hard tissue landmarks and 1.71 mm for 32 soft tissue landmarks. PLS accurately predicted landmarks with low variability. However, AI generally outperformed PLS, particularly for landmarks in the lower part of the craniofacial structure and soft tissue, where uncertainty is considerable. (Angle Orthod. 2025;95:249–258.) |
Kim, Jong-Hak; Moon, Jun-Ho; Roseth, Jeffrey; Suh, Heeyeon; Oh, Heesoo; Lee, Shin-Jae: Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children. In: The Angle Orthodontist, vol. 95, iss. 2, pp. 219-226, 2025. (Type: Journal Article | Abstract | Links | BibTeX | Tags: artificial intelligence, Growth prediction, Longitudinal studies, machine learning)@article{Kim2025,Objectives: To compare differences in craniofacial growth prediction results for Korean and American children according to growth prediction models developed using Korean and American longitudinal growth data. Materials and Methods: Growth prediction models based on cephalometric landmarks were built for each population using longitudinally taken lateral cephalograms of Korean children and American children of northern European origin. The sample sizes of the serial datasets were 679 and 1257 for Korean and American children, respectively. On each cephalogram, 78 cephalometric landmarks were identified. The prediction models were based on the partial least squares method with 160 input and 154 output variables. For each group, growth was predicted by applying the prediction models developed using data from the same and different populations. The growth prediction results were compared and analyzed. Results: The growth prediction results obtained with the prediction model developed using data from the same population were more accurate (P<.0001). The results distinctively visualized the discrepancies in the growth prediction results if different population types were not considered. Conclusions: Applying a growth prediction model generated using data from the same population may be desirable. (Angle Orthod. 2025;95:219–226.) |