@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}

}