Real Alberto Del, Real Octavio Del, Sardina Sebastian, Oyonarte Rodrigo
Graduate Orthodontic Program, Discipline of Orthodontics, Faculty of Odontology, Universidad de los Andes, Santiago, Chile.
Private Practice, Santiago, Chile.
Korean J Orthod. 2022 Mar 25;52(2):102-111. doi: 10.4041/kjod.2022.52.2.102.
To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records.
The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions.
By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used.
The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.
开发并探究一种人工智能系统的实用性，该系统基于性别、模型变量和头影测量记录来预测正畸治疗期间拔牙的必要性。
从一个匿名数据库中获取了214例患者的性别、模型变量和影像学记录，该数据库包含由两位经验丰富的正畸医生治疗的314个病例。数据使用自动化机器学习软件（Auto-WEKA）进行处理，并用于预测拔牙的必要性。
通过生成和比较多个预测模型，基于模型和影像学数据确定是否需要拔牙的准确率达到了93.9%。仅使用模型变量时，准确率为87.4%，而仅使用头影测量信息时，准确率为72.7%。
使用自动化机器学习系统可以生成正畸拔牙预测模型。最佳拔牙预测模型的准确率随着模型和头影测量数据在分析过程中的结合而提高。
