Adelaide Dental School, The University of Adelaide, Adelaide, SA, 5000, Australia.
Sci Rep. 2024 Jul 16;14(1):16423. doi: 10.1038/s41598-024-67640-3.
This study aimed to predict dental freeway space by examining the clinical history, habits, occlusal parameters, mandibular hard tissue movement, soft tissue motion, muscle activity, and temporomandibular joint function of 66 participants. Data collection involved video-based facial landmark tracking, mandibular electrognathography, surface electromyography of mandibular range of motion, freeway space, chewing tasks, phonetic expressions, joint vibration analysis, and 3D jaw scans of occlusion. This resulted in a dataset of 121 predictor features, with freeway space as the target variable. Six models were trained on synthetic data ranging from 500 to 25,000 observations, with 65 original observations reserved for testing: Linear Regression, Random Forest, CatBoost Regressor, XGBoost Regressor, Multilayer Perceptron Neural Network (MPNN), and TabNet. Explainable AI indicated that key predictors of freeway space included phonetics, resting temporalis muscle activity, mandibular muscle activity during clenching, body weight, mandibular hard tissue lateral displacements, and dental arch parameters. CatBoost excelled with a test error of 0.65 mm using 5000 synthetic data points, while a refined MPNN achieved the best performance with 25,000 synthetic data points and 121 unique predictors, yielding an absolute error of 0.43 mm on the 65 original observations.
本研究旨在通过检查 66 名参与者的临床病史、习惯、咬合参数、下颌硬组织运动、软组织运动、肌肉活动和颞下颌关节功能，来预测牙弓间隙。数据收集包括基于视频的面部地标跟踪、下颌电描记术、下颌运动范围的表面肌电图、牙弓间隙、咀嚼任务、语音表达、关节振动分析和咬合的 3D 颌扫描。这产生了一个包含 121 个预测特征的数据集，其中牙弓间隙是目标变量。在合成数据上训练了六个模型，观测值范围从 500 到 25,000，保留了 65 个原始观测值用于测试：线性回归、随机森林、CatBoost 回归器、XGBoost 回归器、多层感知机神经网络 (MPNN) 和 TabNet。可解释性人工智能表明，牙弓间隙的关键预测因素包括语音、颞肌休息时的肌肉活动、咬牙时下颌肌肉的活动、体重、下颌硬组织侧向位移和牙弓参数。CatBoost 在使用 5000 个合成数据点时表现出色，测试误差为 0.65mm，而经过改进的 MPNN 在使用 25000 个合成数据点和 121 个独特预测因子时表现最佳，在 65 个原始观测值上的绝对误差为 0.43mm。
