中国口腔颌面外科杂志

中国口腔颌面外科杂志 ›› 2021, Vol. 19 ›› Issue (2): 156-162.doi: 10.19438/j.cjoms.2021.02.011

• 论著 • 上一篇    下一篇

基于机器学习的智牙拔除后疗效预测及分析

文振宇1, 郭雯瑾2, 冯爱民1, 李龙德2, 文世生3   

  1. 1.南京航空航天大学 计算机科学与技术学院,江苏 南京 211106;
    2.甘肃省嘉峪关市第一人民医院 口腔科,
    3.口腔颌面外科,甘肃 嘉峪关 735100
  • 收稿日期:2020-02-24 修回日期:2020-05-12 出版日期:2021-03-20 发布日期:2021-05-11
  • 通讯作者: 文振宇,E-mail: wzydyx2@163.com
  • 作者简介:文振宇（1994-）,男,硕士研究生

Machine learning based analysis and prediction of curative effect after extraction of wisdom teeth

WEN Zhen-yu1, GUO Wen-jin2, FENG Ai-min1, LI Long-de2, WEN Shi-sheng3   

  1. 1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. Nanjing 211106, Jiangsu Province;
    2. Department of Stomatology,
    3. Department of Oral and Maxillofacial Surgery, The First People's Hospital of Jiayuguan City. Jiayuguan 735100, Gansu Province, China
  • Received:2020-02-24 Revised:2020-05-12 Online:2021-03-20 Published:2021-05-11

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摘要： 目的 通过机器学习算法,分析并预测拔除智牙后并发症的发生情况,为下颌智牙冠周炎的针对性治疗及并发症的早期预防提供科学依据。方法 选择2018年1月—2018年12月在甘肃省嘉峪关第一人民医院口腔科就诊的智牙冠周炎患者467例,其中373例作为训练集,94例作为测试集。分析其数字曲面体层片特点,结合治疗方案及患者随访结果,完成特征收集及数据录入。通过计算各个特征属性的基尼重要性,将所有特征进行重要性排序,进而分析并完成特征选择。采用7种机器学习算法,建立并发症预测评估模型,在训练集上训练至结果收敛,通过10折交叉验证预测的准确率和F1分数,选择最终的算法并进行分析评估。结果 建立包含15个特征属性、1个分类属性的467×16维数据库。经过测试,最终选择随机森林算法作为核心算法,完成并发症预测评估模型。该模型的预测准确率为89%,F1加权平均数为88%。结论 机器学习算法能有效分析病例特征与智牙拔除术后疗效的关系并预测并发症情况,具有较高的临床实用性。

关键词: 机器学习, 智牙, 拔除, 并发症, 预测

Abstract: PURPOSE: To analyze and predict the occurrence of postoperative complications after extraction of wisdom teeth by machine learning algorithms, to provide scientific basis for targeted treatment and early prevention of complications of pericoronitis. METHODS: From January 2018 to December 2018, 467 patients with pericoronitis of wisdom teeth who were treated in the Department of Stomatology of the First People's Hospital of Jiayuguan City, Gansu Province were selected. Among them, 373 patients served as training set and 94 patients served as test set. Feature selection and data were completed through detection of digital panoramic tomography combined with doctor’s treatment plan and patients’ follow-up results. By calculating Gini importance of each feature, all features were sorted by importance, then all features were analyzed and the selection was completed. Seven machine learning algorithms were used to establish a complication predicting model, which was trained on the training set until the result converged. The final algorithm was determined by the accuracy and F1 score of 10-fold cross-validation. RESULTS: A 467×16-dimensional dataset containing 15 feature attributes and 1 classification attribute were established. Random forest was finally selected as the core algorithm to complete the complication predicting model after testing. The accuracy of the final model was 89% and F1 weighted average score was 88%. CONCLUSIONS: Machine learning algorithm can effectively analyze the relationship between case features and curative effect after extraction of wisdom teeth as well as predicting complications, which has high clinical practicability.

Key words: Machine learning, Wisdom teeth, Extraction, Complication, Prediction

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