Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence.

Jun-Ho Moon, Hak-Kyun Shin, Ju-Myung Lee, Sung Joo Cho, Ji-Ae Park, Richard E Donatelli, Shin-Jae Lee
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Abstract

Objectives: To compare facial growth prediction models based on the partial least squares and artificial intelligence (AI).

Materials and methods: Serial longitudinal lateral cephalograms from 410 patients who had not undergone orthodontic treatment but had taken serial cephalograms were collected from January 2002 to December 2022. On every image, 46 skeletal and 32 soft-tissue landmarks were identified manually. Growth prediction models were constructed using multivariate partial least squares regression (PLS) and a deep learning method based on the TabNet deep neural network incorporating 161 predictor, and 156 response, variables. The prediction accuracy between the two methods was compared.

Results: On average, AI showed less prediction error by 2.11 mm than PLS. Among the 78 landmarks, AI was more accurate in 63 landmarks, whereas PLS was more accurate in nine landmarks, including cranial base landmarks. The remaining six landmarks showed no statistical difference between the two methods. Overall, soft-tissue landmarks, landmarks in the mandible, and growth in the vertical direction showed greater prediction errors than hard-tissue landmarks, landmarks in the maxilla, and growth changes in the horizontal direction, respectively.

Conclusions: PLS and AI methods seemed to be valuable tools for predicting growth. PLS accurately predicted landmarks with low variability in the cranial base. In general, however, AI outperformed, particularly for those landmarks in the maxilla and mandible. Applying AI for growth prediction might be more advantageous when uncertainty is considerable.

基于偏最小二乘和人工智能的个性化面部生长预测模型的比较。
目的:比较基于偏最小二乘法和人工智能(AI)的面部生长预测模型。材料和方法:收集2002年1月至2022年12月410名未接受正畸治疗但已进行系列头影检查的患者的系列纵向-横向头影。在每张图像上,人工识别46个骨骼和32个软组织标志。使用多元偏最小二乘回归(PLS)和基于TabNet深度神经网络的深度学习方法构建了增长预测模型,该方法包含161个预测变量和156个响应变量。比较了两种方法的预测精度。结果:AI的预测误差平均比PLS小2.11mm。在78个标志物中,AI在63个标志物上更准确,而PLS在9个标志物(包括颅底标志物)上更准确。其余六个标志在两种方法之间没有统计学差异。总体而言,软组织标志、下颌骨标志和垂直方向的生长分别比硬组织标志、上颌骨标志和水平方向的生长变化显示出更大的预测误差。结论:PLS和AI方法似乎是预测生长的有价值的工具。PLS准确预测了颅底变异性低的标志物。然而,总的来说,人工智能表现出色,尤其是在上颌骨和下颌骨的标志性部位。当不确定性很大时,将人工智能应用于增长预测可能更有利。
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