Huqh Mohamed Zahoor Ul, Abdullah Johari Yap, Al-Rawas Matheel, Husein Adam, Ahmad Wan Muhamad Amir W, Jamayet Nafij Bin, Genisa Maya, Yahya Mohd Rosli Bin
Orthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia.
Craniofacial Imaging Lab, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia.
Diagnostics (Basel). 2023 Sep 22;13(19):3025. doi: 10.3390/diagnostics13193025.
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.
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.
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%.
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.
唇腭裂（CLP）是最常见的先天性颅面畸形，可导致儿童出现多种牙齿异常。本研究的目的是预测上颌牙弓的生长，并为单侧完全性唇腭裂（UCLP）和非UCLP个体开发一个神经网络逻辑回归模型。
本研究采用了一种结合多种方法的新颖方法，如自助法、多层前馈神经网络和有序逻辑回归。基于以下因素创建了一个数据集：年龄和性别等社会人口学特征，以及腭裂类型和与腭裂相关的错牙合类别。训练数据用于创建模型，而测试数据用于验证模型。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腭裂与所选因素之间的潜在关联。
讨论了使用有序逻辑回归的混合技术的结果，其中类别既是因变量又是研究的输出。有序逻辑回归用于将因变量分为三类。所建议的技术表现出色，预测均方误差（PMSE）为2.03%证明了这一点。
研究结果表明，性别、年龄和腭裂之间存在密切关联。UCLP上颌牙弓宽度和长度的差异主要与腭裂的严重程度和面部生长模式有关。
