Department of Orthodontics and Dentofacial Orthopedics, Graduate School of Dentistry, Osaka University, Suita, Osaka, Japan.
Center for Advanced Medical Engineering and Informatics, Osaka University, Suita, Osaka, Japan.
Sci Rep. 2021 Aug 4;11(1):15853. doi: 10.1038/s41598-021-95002-w.
From a socio-psychological standpoint, improving the morphology of the facial soft-tissues is regarded as an important therapeutic goal in modern orthodontic treatment. Currently, many of the algorithms used in commercially available software programs that are said to provide the function of performing profile prediction are based on the false assumption that the amount of movement of hard-tissue and soft-tissue has a proportional relationship. The specification of the proportionality constant value depends on the operator, and there is little evidence to support the validity of the prediction result. Thus, the present study attempted to develop artificial intelligence (AI) systems that predict the three-dimensional (3-D) facial morphology after orthognathic surgery and orthodontic treatment based on the results of previous treatment. This was a retrospective study in a secondary adult care setting. A total of 137 patients who underwent orthognathic surgery (n = 72) and orthodontic treatment with four premolar extraction (n = 65) were enrolled. Lateral cephalograms and 3-D facial images were obtained before and after treatment. We have developed two AI systems to predict facial morphology after orthognathic surgery (System S) and orthodontic treatment (System E) using landmark-based geometric morphometric methods together with deep learning methods; where cephalometric changes during treatment and the coordinate values of the faces before treatment were employed as predictive variables. Eleven-fold cross-validation showed that the average system errors were 0.94 mm and 0.69 mm for systems S and E, respectively. The total success rates, when success was defined by a system error of < 1 mm, were 54% and 98% for systems S and E, respectively. The total success rates when success was defined by a system error of < 2 mm were both 100%. AI systems to predict facial morphology after treatment were therefore confirmed to be clinically acceptable.
从社会心理学的角度来看,改善面部软组织的形态被认为是现代正畸治疗的一个重要治疗目标。目前,许多商业上可用的软件程序中使用的算法都假设硬组织和软组织的运动量之间存在比例关系,而这些算法声称具有预测侧貌的功能。比例常数值的指定取决于操作人员,并且几乎没有证据支持预测结果的有效性。因此,本研究试图开发人工智能 (AI) 系统,根据以往治疗的结果预测正颌手术和正畸治疗后的三维 (3-D) 面部形态。这是一项二级成人护理环境中的回顾性研究。共纳入 137 名接受正颌手术 (n=72) 和四前磨牙拔除正畸治疗 (n=65) 的患者。治疗前后均获得侧颅面片和 3-D 面部图像。我们使用基于标志的几何形态测量方法和深度学习方法开发了两个 AI 系统,分别用于预测正颌手术后 (系统 S) 和正畸治疗后的 (系统 E) 面部形态;其中,治疗过程中的头影测量变化和治疗前面部的坐标值用作预测变量。十一折交叉验证显示,系统 S 和系统 E 的平均系统误差分别为 0.94 毫米和 0.69 毫米。当系统误差 < 1 毫米定义为成功时,系统 S 和系统 E 的总成功率分别为 54%和 98%。当系统误差 < 2 毫米定义为成功时,总成功率均为 100%。因此,证实治疗后预测面部形态的 AI 系统在临床上是可以接受的。