An artificial multilayer perceptron neural network for diagnosis of proximal dental caries
An artificial multilayer perceptron neural network for diagnosis of proximal dental caries
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DOI：
10.1016/j.tripleo.2008.03.002
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发表时间：
2008-12-01
期刊：
影响因子：
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通讯作者：
Felippe Filho, Waldir Neme
中科院分区：
文献类型：
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作者：
Devito, Karina Lopes;Barbosa, Flavio de Souza;Felippe Filho, Waldir Neme
Objective.评估人工智能模型（多层感知器神经网络）的应用是否改善了邻面龋的放射学诊断。研究设计。由25名检查员对160张已拔出的人牙近端表面的X光片进行了关于龋齿存在的评估。检查的X光照片被用来饲料的神经网络，相应的牙齿切片和光学显微镜下评估（金标准）。这个黄金标准用于教导神经网络在放射学检查的基础上诊断龋齿。为了衡量网络的泛化能力，即，其性能与新的情况下，数据被分为3个亚组的训练，测试和交叉验证。受试者工作特征曲线（ROC）下的面积允许比较网络和检查者诊断之间的有效性。在25名检查者中，最佳的ROC曲线面积为0.717，而网络诊断的ROC曲线面积为0.884，表明近端龋诊断有相当大的改善。考虑到所有检查者，使用神经网络的诊断改善率为39.4%。(Oral《口腔外科医学》、《口腔病理学》、《口腔放射学》、《口腔内分泌学》2008年; 106：879-884）
Objective. To evaluate if the application of an artificial intelligence model, a multilayer perceptron neural network, improves the radiographic diagnosis of proximal caries.Study design. One hundred sixty radiographic images of proximal surfaces of extracted human teeth were assessed regarding the presence of caries by 25 examiners. Examination of the radiographs was used to feed the neural network, and the corresponding teeth were sectioned and assessed under optical microscope (gold standard). This gold standard served to teach the neural network to diagnose caries on the basis of the radiographic exams. To gauge the network's capacity for generalization, i.e., its performance with new cases, data were divided into 3 subgroups for training, test, and cross-validation. The area under the receiver operating characteristic (ROC) curve allowed comparison of efficacy between network and examiner diagnosis.Results. For the best of the 25 examiners, the ROC curve area was 0.717, whereas network diagnosis achieved an ROC curve area of 0.884, indicating a sizeable improvement in proximal caries diagnosis.Conclusion. Considering all examiners, the diagnostic improvement using the neural network was 39.4%. (Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2008; 106: 879-884)