PDF(Pubmed)
Abstract：
BACKGROUND: Cleft lip and palate (CLP) are the most common congenital craniofacial deformities that can cause a variety of dental abnormalities in children. The purpose of this study was to predict the maxillary arch growth and to develop a neural network logistic regression model for both UCLP and non-UCLP individuals.
METHODS: This study utilizes a novel method incorporating many approaches, such as the bootstrap method, a multi-layer feed-forward neural network, and ordinal logistic regression. A dataset was created based on the following factors: socio-demographic characteristics such as age and gender, as well as cleft type and category of malocclusion associated with the cleft. Training data were used to create a model, whereas testing data were used to validate it. The study is separated into two phases: phase one involves the use of a multilayer neural network and phase two involves the use of an ordinal logistic regression model to analyze the underlying association between cleft and the factors chosen.
RESULTS: The findings of the hybrid technique using ordinal logistic regression are discussed, where category acts as both a dependent variable and as the study\'s output. The ordinal logistic regression was used to classify the dependent variables into three categories. The suggested technique performs exceptionally well, as evidenced by a Predicted Mean Square Error (PMSE) of 2.03%.
CONCLUSIONS: The outcome of the study suggests that there is a strong association between gender, age, and cleft. The difference in width and length of the maxillary arch in UCLP is mainly related to the severity of the cleft and facial growth pattern.
METHODS: This study utilizes a novel method incorporating many approaches, such as the bootstrap method, a multi-layer feed-forward neural network, and ordinal logistic regression. A dataset was created based on the following factors: socio-demographic characteristics such as age and gender, as well as cleft type and category of malocclusion associated with the cleft. Training data were used to create a model, whereas testing data were used to validate it. The study is separated into two phases: phase one involves the use of a multilayer neural network and phase two involves the use of an ordinal logistic regression model to analyze the underlying association between cleft and the factors chosen.
RESULTS: The findings of the hybrid technique using ordinal logistic regression are discussed, where category acts as both a dependent variable and as the study\'s output. The ordinal logistic regression was used to classify the dependent variables into three categories. The suggested technique performs exceptionally well, as evidenced by a Predicted Mean Square Error (PMSE) of 2.03%.
CONCLUSIONS: The outcome of the study suggests that there is a strong association between gender, age, and cleft. The difference in width and length of the maxillary arch in UCLP is mainly related to the severity of the cleft and facial growth pattern.
摘要：
背景：唇腭裂（CLP）是最常见的先天性颅面畸形，可导致儿童多种牙齿异常。这项研究的目的是预测上颌弓的生长，并为UCLP和非UCLP个体开发神经网络逻辑回归模型。
方法：本研究采用了一种新颖的方法，结合了许多方法，例如引导方法，多层前馈神经网络，和序数逻辑回归。基于以下因素创建数据集：社会人口统计学特征，如年龄和性别，以及与left裂相关的left裂类型和错牙合类型。训练数据用于创建模型，而测试数据被用来验证它。该研究分为两个阶段：第一阶段涉及使用多层神经网络，第二阶段涉及使用有序逻辑回归模型来分析裂隙与所选因素之间的潜在关联。
结果：讨论了使用序数逻辑回归的混合技术的发现，其中类别既作为因变量，又作为研究的输出。序数逻辑回归用于将因变量分为三类。建议的技术表现非常好，由2.03%的预测均方误差(PMSE)证明。
结论：研究结果表明，性别之间存在很强的关联，年龄,和裂口。UCLP上颌弓宽度和长度的差异主要与裂隙的严重程度和面部生长方式有关。
方法：本研究采用了一种新颖的方法，结合了许多方法，例如引导方法，多层前馈神经网络，和序数逻辑回归。基于以下因素创建数据集：社会人口统计学特征，如年龄和性别，以及与left裂相关的left裂类型和错牙合类型。训练数据用于创建模型，而测试数据被用来验证它。该研究分为两个阶段：第一阶段涉及使用多层神经网络，第二阶段涉及使用有序逻辑回归模型来分析裂隙与所选因素之间的潜在关联。
结果：讨论了使用序数逻辑回归的混合技术的发现，其中类别既作为因变量，又作为研究的输出。序数逻辑回归用于将因变量分为三类。建议的技术表现非常好，由2.03%的预测均方误差(PMSE)证明。
结论：研究结果表明，性别之间存在很强的关联，年龄,和裂口。UCLP上颌弓宽度和长度的差异主要与裂隙的严重程度和面部生长方式有关。
