American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics · 2023
XGBoost-aided prediction of lip prominence based on hard-tissue measurements and demographic characteristics in an Asian population.
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Xing Lu, Zhang Xiaoqi, Guo Yongwen, Bai Ding, Xu Hui
Аннотация
INTRODUCTION: Prediction of lip prominence based on hard-tissue measurements could be helpful in orthodontic treatment planning and has been challenging and formidable thus far.
METHODS: A machine learning-based cross-sectional study was conducted on 1549 patients. Hard-tissue measurements and demographic information were used as the input features. Seven popular machine learning algorithms were applied to the datasets to predict upper and lower lip prominence. The algorithm that performed the best was selected for the construction of the prediction model. Evaluation of feature importance was conducted using 3 classical methods.
RESULTS: Among the 7 algorithms, the XGBoost model performed the best in the prediction of the distances between labrale superius or labrale inferius to the esthetics plane (UL-EP and LL-EP distances), with root mean square error values of 1.25, 1.49 and r2 values of 0.755 and 0.683, respectively. Among the 14 input features, the L1-NB distance contributed the most to the prominences of the upper and lower lips. A lip prominence predictor was developed to facilitate clinical application by deploying the prediction model into a downloadable tool kit.
CONCLUSIONS: The XGBoost model performed well with high accuracy and practicability in predicting upper and lower lip prominence. The artificial intelligence-aided predictor could serve as a reference for orthodontic treatment planning.
METHODS: A machine learning-based cross-sectional study was conducted on 1549 patients. Hard-tissue measurements and demographic information were used as the input features. Seven popular machine learning algorithms were applied to the datasets to predict upper and lower lip prominence. The algorithm that performed the best was selected for the construction of the prediction model. Evaluation of feature importance was conducted using 3 classical methods.
RESULTS: Among the 7 algorithms, the XGBoost model performed the best in the prediction of the distances between labrale superius or labrale inferius to the esthetics plane (UL-EP and LL-EP distances), with root mean square error values of 1.25, 1.49 and r2 values of 0.755 and 0.683, respectively. Among the 14 input features, the L1-NB distance contributed the most to the prominences of the upper and lower lips. A lip prominence predictor was developed to facilitate clinical application by deploying the prediction model into a downloadable tool kit.
CONCLUSIONS: The XGBoost model performed well with high accuracy and practicability in predicting upper and lower lip prominence. The artificial intelligence-aided predictor could serve as a reference for orthodontic treatment planning.