Parrish Matthew, O'Connell Ella, Eckert George, Hughes Jay, Badirli Sarkhan, Turkkahraman Hakan
Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA.
Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA.
Diagnostics (Basel). 2023 Aug 22;13(17):2729. doi: 10.3390/diagnostics13172729.
The aim of this study was to create a novel machine learning (ML) algorithm for predicting the post-pubertal mandibular length and -axis in females. Cephalometric data from 176 females with Angle Class I occlusion were used to train and test seven ML algorithms. For all ML methods tested, the mean absolute errors (MAEs) for the 2-year prediction ranged from 2.78 to 5.40 mm and 0.88 to 1.48 degrees, respectively. For the 4-year prediction, MAEs of mandibular length and -axis ranged from 3.21 to 4.00 mm and 1.19 to 5.12 degrees, respectively. The most predictive factors for post-pubertal mandibular length were mandibular length at previous timepoints, age, sagittal positions of the maxillary and mandibular skeletal bases, mandibular plane angle, and anterior and posterior face heights. The most predictive factors for post-pubertal -axis were -axis at previous timepoints, mandibular plane angle, and sagittal positions of the maxillary and mandibular skeletal bases. ML methods were identified as capable of predicting mandibular length within 3 mm and -axis within 1 degree. Compared to each other, all of the ML algorithms were similarly accurate, with the exception of multilayer perceptron regressor.
本研究的目的是创建一种新颖的机器学习（ML）算法，用于预测女性青春期后下颌骨长度和轴。来自176名安氏I类咬合女性的头影测量数据用于训练和测试七种ML算法。对于所有测试的ML方法，2年预测的平均绝对误差（MAE）分别为2.78至5.40毫米和0.88至1.48度。对于4年预测，下颌骨长度和轴的MAE分别为3.21至4.00毫米和1.19至5.12度。青春期后下颌骨长度的最具预测性因素是先前时间点的下颌骨长度、年龄、上颌和下颌骨骼基底的矢状位置、下颌平面角以及前、后脸高。青春期后轴的最具预测性因素是先前时间点的轴、下颌平面角以及上颌和下颌骨骼基底的矢状位置。ML方法被确定能够在3毫米内预测下颌骨长度，在1度内预测轴。相互比较时，除多层感知器回归器外，所有ML算法的准确性相似。
