Determination of sexual dimorphism with CBCT images of the frontal sinus using a predictive formula and an artificial neural network.

IF 2.2 3区 医学 Q2 DENTISTRY, ORAL SURGERY & MEDICINE
Journal of Applied Oral Science Pub Date : 2025-06-27 eCollection Date: 2025-01-01 DOI:10.1590/1678-7757-2025-0049
Julyana de Araújo Oliveira, Natália Rogério Borella, Flávia Maria de Moraes Ramos-Perez, Andrea Dos Anjos Pontual, Maria Alice Andrade Calazans, Felipe Alberto Barbosa Simão Ferreira, Francisco Madeiro, Maria Luiza Dos Anjos Pontual
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引用次数: 0

Abstract

Objective: this study aims to evaluate the sexual dimorphism of the morphometric features of the frontal sinus via cone beam computed tomography (CBCT) reconstructions, using a predictive formula and an artificial neural network (ANN).

Methodology: the morphometric features of the frontal sinuses obtained from 1,000 CBCT scans, equally distributed by sex, were assessed by two examiners. The frontal sinus morphometric features from 800 CBCT scans were analyzed using Mann-Whitney tests and a multivariate logistic regression model to identify key morphometric features for sex determination and to develop the predictive formula. These features were subsequently used to validate the predictive formula and the machine learning-based classification system. The predictive formula was evaluated using a set of 200 CBCT scans. The machine learning-based classification system consisted of a three-layer ANN trained with 80% of the CBCT scans and tested with the remaining 20%.

Results: Except for the higher frontal sinus index in females, males exhibited higher numerical values for height, width, and anteroposterior (AP) length. The significance level for all statistical tests was set at 0.05. Multivariate logistic regression identified the following four essential morphometric features: sinus height, anteroposterior length (depth) of the sinus, sinus width, and total sinus width. Both the predictive formula and the ANN demonstrated sexual dimorphism. The accuracy, specificity, sensitivity, precision, and F1- score values were 73.50%, 74.00%, 73.00%, 73.74%, and 73.37% for the regression model, and 76.00%, 84.00%, 68.00%, 80.95%, and 73.91% for the ANN, respectively. Except for sensitivity, the ANN outperformed the predictive formula regarding maximum specificity, accuracy, precision, and F1 score.

Conclusion: both methods, particularly the ANN, can potentially support sex estimation in the Brazilian forensic context.

利用预测公式和人工神经网络确定额窦CBCT图像的性别二态性。
目的:本研究旨在通过锥形束计算机断层扫描(CBCT)重建,利用预测公式和人工神经网络(ANN)评估额窦形态特征的性别二态性。方法:从1000个CBCT扫描中获得额窦的形态特征,按性别均匀分布,由两名检查人员评估。使用Mann-Whitney检验和多元逻辑回归模型分析800个CBCT扫描的额窦形态特征,以确定性别确定的关键形态特征并开发预测公式。这些特征随后被用于验证预测公式和基于机器学习的分类系统。使用200次CBCT扫描对预测公式进行评估。基于机器学习的分类系统由一个三层神经网络组成,该神经网络使用80%的CBCT扫描进行训练,并使用剩余的20%进行测试。结果:除女性额窦指数较高外,男性额窦的高度、宽度和前后位长度均高于女性。所有统计检验的显著性水平设为0.05。多元逻辑回归确定了以下四个基本形态特征:鼻窦高度、鼻窦前后长度(深度)、鼻窦宽度和鼻窦总宽度。预测公式和人工神经网络均表现出性别二态性。回归模型的准确率、特异度、灵敏度、精密度和F1-评分值分别为73.50%、74.00%、73.00%、73.74%和73.37%,神经网络的准确率分别为76.00%、84.00%、68.00%、80.95%和73.91%。除敏感性外,人工神经网络在最大特异性、准确性、精密度和F1评分方面都优于预测公式。结论:这两种方法,特别是人工神经网络,可以潜在地支持巴西法医背景下的性别估计。
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来源期刊
Journal of Applied Oral Science
Journal of Applied Oral Science 医学-牙科与口腔外科
CiteScore
4.80
自引率
3.70%
发文量
46
审稿时长
4-8 weeks
期刊介绍: The Journal of Applied Oral Science is committed in publishing the scientific and technologic advances achieved by the dental community, according to the quality indicators and peer reviewed material, with the objective of assuring its acceptability at the local, regional, national and international levels. The primary goal of The Journal of Applied Oral Science is to publish the outcomes of original investigations as well as invited case reports and invited reviews in the field of Dentistry and related areas.
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