Brickley M R, Shepherd J P
Department of Oral Surgery, Dental School, University of Wales College of Medicine, Heath Park, Cardiff.
Med Decis Making. 1996 Apr-Jun;16(2):153-60. doi: 10.1177/0272989X9601600207.
The authors developed and tested 12 neural networks of different architectures to make lower-third-molar treatment-planning decisions, using a software-based neural network (Neudesk 1.2, Neural Computer Sciences, Southampton, UK). Network training was undertaken using clinical histories from 119 patients (with 238 lower third molars) referred for treatment planning (79 females and 40 males, mean age 25 years) together with output data consisting of actual treatments planned by a senior oral surgeon. Both the input clinical data and the consultant decisions were treated on a tooth-wise basis and were coded to numerical values. Binary data (e.g., present/absent) were coded to 1 and 0, while quantitative data (e.g., age) were scaled to fall between 0 and 1. A network based on the optimal architecture was trained and then interrogated with test data derived from a further 174 patients (119 females and 55 males, mean age 26 years) with 348 lower third molars. Network decisions were dichotomized with a threshold of 0.8. With no knowledge of the network decisions, the senior oral surgeon indicated his preferred treatments. The teeth were then assigned to "gold-standard" categories of indications present or absent based on National Institutes of Health consensus criteria. Against this, the network achieved a sensitivity of 0.78, which was slightly inferior to that of the oral surgeon (0.88), although this difference was not significant, and a specificity of 0.98, compared with 0.99 for the oral surgeon (p = NS). Agreement between the oral surgeon and network decisions was very high (kappa = 0.850). This study demonstrates that it is possible to train a neural network to provide reliable decision support for lower-third-molar treatment planning.
作者开发并测试了12种不同架构的神经网络，用于做出下颌第三磨牙治疗计划的决策，使用的是基于软件的神经网络（Neudesk 1.2，英国南安普敦神经计算机科学公司）。网络训练使用了119例患者（238颗下颌第三磨牙）的临床病史，这些患者因治疗计划前来就诊（79名女性和40名男性，平均年龄25岁），同时还使用了由一位资深口腔外科医生制定的实际治疗计划作为输出数据。输入的临床数据和专家决策均按牙齿逐一处理，并编码为数值。二元数据（如存在/不存在）编码为1和0，而定量数据（如年龄）则进行缩放，使其介于0和1之间。基于最优架构的网络经过训练，然后用另外174例患者（119名女性和55名男性，平均年龄26岁）的348颗下颌第三磨牙的测试数据进行检验。网络决策以0.8的阈值进行二分。在不了解网络决策的情况下，资深口腔外科医生表明了他的首选治疗方案。然后根据美国国立卫生研究院的共识标准，将牙齿分为存在或不存在适应证的“金标准”类别。相比之下，该网络的灵敏度为0.78，略低于口腔外科医生（0.88），尽管这种差异不显著，特异性为0.98，而口腔外科医生为0.99（p =无显著性差异）。口腔外科医生和网络决策之间的一致性非常高（kappa = 0.850）。这项研究表明，训练神经网络为下颌第三磨牙治疗计划提供可靠的决策支持是可行的。
