Anic-Milosevic Sandra, Medancic Natasa, Calusic-Sarac Martina, Dumancic Jelena, Brkic Hrvoje
Department of Orthodontics, School of Dental Medicine, University of Zagreb, Zagreb, Croatia.
Private Practice Policlinic IMED, Zagreb, Croatia.
Korean J Orthod. 2023 May 25;53(3):194-204. doi: 10.4041/kjod22.250.
To investigate sex-specific correlations between the dimensions of permanent canines and the anterior Bolton ratio and to construct a statistical model capable of identifying the sex of an unknown subject.
Odontometric data were collected from 121 plaster study models derived from Caucasian orthodontic patients aged 12-17 years at the pretreatment stage by measuring the dimensions of the permanent canines and Bolton's anterior ratio. Sixteen variables were collected for each subject: 12 dimensions of the permanent canines, sex, age, anterior Bolton ratio, and Angle's classification. Data were analyzed using inferential statistics, principal component analysis, and artificial neural network modeling.
Sex-specific differences were identified in all odontometric variables, and an artificial neural network model was prepared that used odontometric variables for predicting the sex of the participants with an accuracy of > 80%. This model can be applied for forensic purposes, and its accuracy can be further improved by adding data collected from new subjects or adding new variables for existing subjects. The improvement in the accuracy of the model was demonstrated by an increase in the percentage of accurate predictions from 72.0-78.1% to 77.8-85.7% after the anterior Bolton ratio and age were added.
The described artificial neural network model combines forensic dentistry and orthodontics to improve subject recognition by expanding the initial space of odontometric variables and adding orthodontic parameters.
研究恒牙尖牙尺寸与前牙 Bolton 比率之间的性别特异性相关性，并构建一个能够识别未知个体性别的统计模型。
通过测量恒牙尖牙尺寸和 Bolton 前牙比率，从 121 个来自 12 - 17 岁高加索正畸患者治疗前阶段的石膏研究模型中收集牙测量数据。为每个受试者收集 16 个变量：恒牙尖牙的 12 个尺寸、性别、年龄、前牙 Bolton 比率和安氏分类。使用推断统计、主成分分析和人工神经网络建模对数据进行分析。
在所有牙测量变量中均发现了性别特异性差异，并制备了一个人工神经网络模型，该模型使用牙测量变量预测参与者的性别，准确率超过 80%。该模型可用于法医目的，通过添加从新受试者收集的数据或为现有受试者添加新变量，其准确率可进一步提高。在前牙 Bolton 比率和年龄加入后，准确预测的百分比从 72.0 - 78.1%增加到 77.8 - 85.7%，证明了模型准确率的提高。
所描述的人工神经网络模型将法医牙科学和正畸学相结合，通过扩展牙测量变量的初始空间并添加正畸参数来提高个体识别能力。
