Diagnosis of Tooth Prognosis Using Artificial Intelligence.
Diagnosis of Tooth Prognosis Using Artificial Intelligence.
复制标题
DOI：
10.3390/diagnostics12061422
复制
发表时间：
2022-06-09
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
个别牙齿预后的准确诊断必须在考虑更广泛的治疗计划的情况下综合确定。本研究的目的是建立一个有效的人工智能（AI）为基础的模块，准确的牙齿预后决策的基础上，哈佛牙科医学院（HSDM）的综合治疗计划课程（CTPC）。根据HSDM-CTPC中的17个临床决定因素，将94例2359颗患牙分为A、B两组（A组16名，B组13名），进行1 ~ 5个等级（1-无望，5-长期良好）的牙齿预后评价。三种人工智能机器学习方法，包括梯度提升分类器，决策树分类器和随机森林分类器被用来创建一个算法。以三位有经验的口腔修复医师共同确定的金标准数据为参照，对这三种方法进行评价，并分析其准确性。决策树分类器的最高准确度为0.8413（模型A）和0.7523（模型B）。梯度提升分类器和随机森林分类器的准确率分别为0.6896、0.6687和0.8413、0.7523。总体而言，决策树分类器具有最好的准确率在三种方法。该研究有助于在考虑治疗计划的牙齿预后决策过程中实施AI。
The accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels (1—Hopeless, 5—Good condition for long term) by two groups (Model-A with 16, and Model-B with 13 examiners) based on 17 clinical determining factors selected from the HSDM-CTPC. Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm. These three methods were evaluated against the gold standard data determined by consensus of three experienced prosthodontists, and their accuracy was analyzed. The decision tree classifier indicated the highest accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient boosting classifier and the random forest classifier was 0.6896, 0.6687, and 0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best accuracy among the three methods. The study contributes to the implementation of AI in the decision-making process of tooth prognosis in consideration of the treatment plan.
登录
查看更多内容
影响因子：
1.9
作者：
Chen, Yo-Wei;Stanley, Kyle;Att, Wael
通讯作者：
Att, Wael
影响因子：
4.3
作者：
Pjetursson, Bjarni E.;Sailer, Irena;Haemmerle, Christoph H. F.
通讯作者：
Haemmerle, Christoph H. F.
DOI：
10.1016/j.tripleo.2008.03.002
发表时间：
2008-12-01
期刊：
ORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY AND ENDODONTOLOGY
影响因子：
--
作者：
Devito, Karina Lopes;Barbosa, Flavio de Souza;Felippe Filho, Waldir Neme
通讯作者：
Felippe Filho, Waldir Neme
影响因子：
4.3
作者：
HIRSCHFELD, L;WASSERMAN, B
通讯作者：
WASSERMAN, B
DOI：
10.14219/jada.archive.1995.0308
发表时间：
1995-07-01
影响因子：
3.9
作者：
DUNCAN, RC;HEAVEN, T;PATEL, JR
通讯作者：
PATEL, JR