基于人工神经网络的错牙合畸形减数矫治的辅助鉴别

doi:10.13701/j.cnki.kqyxyj.2016.12.020

口腔医学研究 ›› 2016, Vol. 32 ›› Issue (12): 1308-1311.DOI: 10.13701/j.cnki.kqyxyj.2016.12.020

基于人工神经网络的错牙合畸形减数矫治的辅助鉴别

韩磊¹,谢晓秋^1*,王阿明²,赵丹¹

1. 南京大学医学院附属口腔医院正畸科江苏南京 210008;
2. 徐州医科大学江苏徐州 221004

收稿日期:2016-08-18 出版日期:2016-12-25 发布日期:2016-12-26
通讯作者: 谢晓秋,电话:(025)83620282
作者简介:韩磊(1983~ ),男,山西人,硕士,主治医师,主要从事骨生物学研究工作。

Artificial Neural Network Assisted Selection of Extractionor Non-extraction Orthodontic Treatment for Malocclusion.

HAN Lei¹, XIE Xiao-qiu^1*, WANG A-ming², ZHAO Dan¹.

1. Department of Orthodontics, Stomatology HospitalAffiliated to Medical School, Nanjing University, Nanjing210008, China;
2. Department of Mathematics Physics, Xuzhou Medical College, Xuzhou221004,China

Received:2016-08-18 Online:2016-12-25 Published:2016-12-26

摘要/Abstract

摘要： 目的:借助人工神经网络方法建立错牙合畸形病例正畸治疗减数与否决策建议的专家系统。方法:选取错牙合畸形病例230例,其中减数病例138例及未减数者92例,每个病例用28个指标反映。在上述230个病例中选取207个病例的集合,利用MATLAB语言,构成28-25-1BP人工神经网络模型的训练集,另23个病例作为测试集。结果:建立了230例错牙合畸形患者资料数据库;对学习训练过的207例数据进行测试,结果其正确率为100%,然后再用未经学习训练过的23个测试集样本进行测试,结果有19例预测成功,正确率为82.6%(4个病例预测失败)。结论:所建立的人工神经网络能够对错牙合畸形病例正畸治疗是否减数做出82.6%正确的结果。

关键词: 人工智能, 人工神经网络, 减数, 错牙合畸形

Abstract: Objective: To use artificial neural networks (ANN) to create a decision-making expert system (ES) for determining whether extraction is needed in the orthodontic treatment of malocclusion patients. Methods: A total of 230 subjects were examined, of which 138 underwent extraction treatment and 92 underwent non-extraction treatment. In each case, 28 indices were ex-amined. Data from 207 patients were aggregated to construct the training set, and data from the other 23 patients were used as the testing set. A Back Propagation (BP) ANN model was constructed using the MATLAB language to assist in evaluating the necessity for extraction. Results: When data from the training set were tested, the ES was 100% correct. When data from the testing set were submitted to the ES, the treatment plan was correctly fore-casted in 82.6% of the cases (i.e., only 4 cases failed). Conclusion: The constructed ANN was useful in determining whether extraction or non-extraction treatment should be employed in malocclusion patients.

Key words: Artificial intel ligence , Neutral network, Tooth extraction, Malocclusion

中图分类号:

R783.5

韩磊,谢晓秋,王阿明,赵丹. 基于人工神经网络的错牙合畸形减数矫治的辅助鉴别[J]. 口腔医学研究, 2016, 32(12): 1308-1311.

HAN Lei, XIE Xiao-qiu, WANG A-ming, ZHAO Dan.. Artificial Neural Network Assisted Selection of Extractionor Non-extraction Orthodontic Treatment for Malocclusion.[J]. Journal of Oral Science Research, 2016, 32(12): 1308-1311.

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基于人工神经网络的错牙合畸形减数矫治的辅助鉴别

Artificial Neural Network Assisted Selection of Extractionor Non-extraction Orthodontic Treatment for Malocclusion.

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