矫形牙科临床决策支持神经网络

Pavel M. Ignatov, A. Oleynikov, Alexander V. Gus’kov, Alina L. Shlykova, Dmitrii A. Surov
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引用次数: 0

摘要

背景:当代牙科中使用的人工智能软件能够根据治疗条件自主选择修复结构,并根据 X 射线和口内颌骨扫描数据进行诊断。机器学习领域的神经网络是一种数学模型,它采用了生物体内神经网络的原理。它能够根据权重系数处理输入信号,通过特定的层数,并在输出端形成正确的答案。这个答案与输出层中激活函数值最大的神经元相对应。目的:本研究旨在开发一种用于骨科治疗计划临床决策的神经网络。材料与方法:使用 Processing 编程环境和类 C 编程语言构建了一个神经网络。在网络训练阶段,确定了隐藏层的数量,选择了训练系数,并确定了训练历元的数量。网络训练采用误差反向传播法,即计算网络的均方根误差,通过神经网络反向传播信号,并根据学习系数调整加权系数。输入层(向量)包括临床条件[1, 2]:口腔状况、过敏性鼻炎和各种临床表现(牙面破坏指数、牙齿活力等)。输出层的维度取决于所用结构的数量,共有 19 个神经元(修复体包括滴定管式、伸缩式、盖式、板式;微型修复体按类型分列,如台式、覆盖式和镶嵌式)。输出层包括活动和固定假体,根据预先设计的算法进行选择。该算法基于以下临床条件: 保留牙齿的状况和数量 咀嚼牙齿咬合面破坏指数 布莱克龋洞分类 副功能、过敏史[3, 4]。 结果:开发了一种神经网络算法,要求医生在口腔检查后输入临床数据。该神经网络可协助临床决策,在每一层进行数学计算,将输入向量(以及随后的每一层)的元素与加权系数(通过训练神经网络获得)相乘,并添加偏差。为了获得激活函数计算区域内的结果,通过激活函数(Sigmoid、ReLu)对获得的结果进行,选择结果最大的输出神经元,预测最合适的设计[5, 6]。结论:因此,考虑到不同假体的潜在用途,所开发的神经网络能够针对不同病例提出临床上合理的矫形治疗方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A neural network for clinical decision support in orthopedic dentistry
BACKGROUND: Artificial intelligence software used in contemporary dentistry is capable of autonomously selecting prosthetic structures based on treatment conditions, establishing a diagnosis based on X-ray and intraoral jaw scanning data. A neural network in the field of machine learning is a mathematical model that employs the principles of a neural network found in living organisms. It is capable of processing input signals in accordance with weight coefficients, passing them through a specific number of layers, and forming the correct answer at the output. This answer corresponds to the neuron of the output layer with the highest value of the activation function. AIM: The aim of the study was to develop a neural network for clinical decision making in orthopedic treatment planning. MATERIALS AND METHODS: A neural network was constructed using the Processing programming environment and a C-like programming language. At the stage of network training, the number of hidden layers was determined, the training coefficient was selected, and the number of training epochs was determined. The network was trained using the backpropagation of error method, which involved calculating the root-mean-square error of the network, backpropagating the signal through the neural network, and adjusting the weighting coefficients in consideration of the learning coefficient. The input layer (vector) comprised clinical conditions [1, 2]: oral cavity condition, allergoanamnesis, and various manifestations of the clinical picture (index of destruction of tooth surfaces, vitality of teeth, etc.). The dimensionality of the output layer was dependent on the number of constructions used and amounted to 19 neurons (prostheses including burette, telescopic, cover, plate; microprostheses by type such as table-top, overlay, and inlay). The output layer consisted of removable and fixed prostheses, the selection of which was based on a pre-designed algorithm. This algorithm was based on the following clinical conditions: Condition and number of teeth retained Index of destruction of the occlusal surface of masticatory teeth Black’s classification of carious cavities Parafunctions, allergic history [3, 4]. RESULTS: A neural network algorithm was developed in which a physician was required to input clinical data following an oral examination. The neural network, which facilitates clinical decision-making assistance, performs mathematical calculations in each layer, multiplying the elements of the input vector (and subsequently, each layer) by weighting coefficients (obtained as a result of training the neural network), and adding a bias. In order to obtain the results in the area of the activation function calculation, the obtained result was conducted through the activation function (Sigmoid, ReLu), selecting the output neuron with the largest result and predicting the most appropriate design [5, 6]. CONCLUSIONS: Consequently, the developed neural network is capable of proposing clinically justified variations of orthopedic treatment plans in individual cases, taking into account the potential use of different prostheses.
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