Choi Hyuk-Il, Jung Seok-Ki, Baek Seung-Hak, Lim Won Hee, Ahn Sug-Joon, Yang Il-Hyung, Kim Tae-Woo
Department of Orthodontics, School of Dentistry, Seoul National University, Seoul.
Department of Orthodontics, Korea University Ansan Hospital, Ansan.
J Craniofac Surg. 2019 Oct;30(7):1986-1989. doi: 10.1097/SCS.0000000000005650.
Diagnosis and treatment planning are the most important steps in the orthognathic surgery for the successful treatment. The purpose of this study was to develop a new artificial intelligent model for surgery/non-surgery decision and extraction determination, and to evaluate the performance of this model. The sample used in this study consisted of 316 patients in total. Of the total sample, 160 were planned with surgical treatment and 156 were planned with non-surgical treatment. The input values of artificial neural network were obtained from 12 measurement values of the lateral cephalogram and 6 additional indexes. The artificial intelligent model of machine learning consisted of 2-layer neural network with one hidden layer. The learning was carried out in 3 stages, and 4 best performing models were adopted. Using these models, decision-making success rates of surgery/non-surgery, surgery type, and extraction/non-extraction were calculated. The final diagnosis success rate was calculated by comparing the actual diagnosis with the diagnosis obtained by the artificial intelligent model. The success rate of the model showed 96% for the diagnosis of surgery/non-surgery decision, and showed 91% for the detailed diagnosis of surgery type and extraction decision. This study suggests the artificial intelligent model using neural network machine learning could be applied for the diagnosis of orthognathic surgery cases.
诊断和治疗计划是正颌外科成功治疗的最重要步骤。本研究的目的是开发一种用于手术/非手术决策和拔牙确定的新型人工智能模型，并评估该模型的性能。本研究使用的样本总共包括316名患者。在总样本中，160名计划进行手术治疗，156名计划进行非手术治疗。人工神经网络的输入值来自头颅侧位片的12个测量值和6个附加指标。机器学习的人工智能模型由具有一个隐藏层的两层神经网络组成。学习分三个阶段进行，并采用了4个性能最佳的模型。使用这些模型，计算手术/非手术、手术类型和拔牙/不拔牙的决策成功率。通过将实际诊断与人工智能模型获得的诊断进行比较，计算最终诊断成功率。该模型在手术/非手术决策诊断方面的成功率为96%，在手术类型和拔牙决策的详细诊断方面的成功率为91%。本研究表明，使用神经网络机器学习的人工智能模型可应用于正颌外科病例的诊断。
