Gender estimation using machine learning algorithms and artificial neural networks based on parameters obtained from the sphenoid sinus.
Objective: The aim of this study is to estimate gender using parameters obtained from the sphenoid sinus in computed tomography (CT) images, utilizing Machine Learning (ML) algorithms and Artificial Neural Networks (ANNs). Method: In this study, length, width, and volume measurements of the sphenoid...
| Published in: | Cirugía y Cirujanos Vol. 94; no. 1; pp. 93 - 100 |
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| Main Authors: | , , , , , |
| Format: | Article |
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Publicidad Permanyer SLU
jan/feb2026
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=lth&AN=191956245&site=ehost-live header: @attributes: shortDbName: lth uiTerm: 191956245 longDbName: MedicLatina uiTag: AN controlInfo: bkinfo: jinfo: jid: 00097411 7KK jtl: Cirugía y Cirujanos issn: 00097411 maglogo: N pubinfo: dt: jan/feb2026 vid: 94 iid: 1 pid: 37650 pub: Publicidad Permanyer SLU artinfo: ui: 191956245 10.24875/CIRU.24000558 ppf: 93 ppct: 7 formats: tig: atl: Gender estimation using machine learning algorithms and artificial neural networks based on parameters obtained from the sphenoid sinus. aug: au: Harmandaoglu, Oguzhan Secgin, Yusuf Kaya, Seren Senol, Deniz Oner, Zulal Onbas, Omer affil: Department of Therapy And Rehabilitation, Çatalzeytin Vocational School, Kastamonu University, Kastamonu, Turkey Department of Anatomy, Faculty of Medicine, Karabük University, Karabük, Turkey Department of Anatomy, Faculty of Medicine, Duzce University, Düzce, Turkey Department of Anatomy, Faculty of Medicine, Izmir Bakırçay University, İzmir, Turkey Department of Radiology, Faculty of Medicine, Düzce University, Düzce. Turkey su: Machine learning Artificial neural networks Morphometrics Classification algorithms Sphenoid sinus Diagnostic sex determination Computed tomography sug: subj: Machine learning Artificial neural networks Morphometrics Classification algorithms Sphenoid sinus Diagnostic sex determination Computed tomography keyword: Gender estimation Machine learning algorithms Algoritmos de aprendizaje automático Predicción del sexo Redes neuronales artificiales Seno esfenoidal ab: Objective: The aim of this study is to estimate gender using parameters obtained from the sphenoid sinus in computed tomography (CT) images, utilizing Machine Learning (ML) algorithms and Artificial Neural Networks (ANNs). Method: In this study, length, width, and volume measurements of the sphenoid sinus were evaluated from CT images of 300 individuals (150 males and 150 females) aged 18-65 years. Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis, Logistic Regression (LR), Extra Tree Classifier, Random Forest, Decision Tree (DT), Gaussian Naive Bayes (GaussianNB), K-Nearest Neighbors (k-NN) algorithms, and ANN model were used for gender prediction. Results: The length, width, and volume of the sphenoid sinus on both the left and right sides were found to be significantly higher in males compared to females (p < 0.05). The performance values of the ML algorithms were found as follows: LDA 0.82; k-NN 0.80; LR 0.84; GaussianNB 0.80; DT 0.82; and ANN 0.82. Conclusions: Morphometric measurements of the sphenoid sinus, when analyzed with the LDA, LR, DT, and ANN algorithms, showed high accuracy and provided reliable data for sex estimation. Objetivo: Predecir el sexo mediante el uso de algoritmos de aprendizaje automático y redes neuronales artificiales a partir de parámetros del seno esfenoidal medidos en imágenes de tomografía computarizada (TC). Método: Se evaluaron las mediciones de longitud, ancho y volumen del seno esfenoidal de imágenes de TC de 300 individuos, 150 hombres y 150 mujeres, con un rango de edad de 18 a 65 años. Se utilizaron los algoritmos de análisis discriminante lineal (LDA), análisis discriminante cuadrático (QDA), regresión logística (LR), clasificador de árbol extra (ETC), bosque aleatorio (RF), árbol de decisión (DT), Gaussian Naive Bayes (GaussianNB), vecinos más próximos (k-NN) y modelo de redes neuronales artificiales (ANN) para la predicción del sexo. Resultados: Las longitudes, anchuras y volúmenes del seno esfenoidal izquierdo y derecho fueron significativamente mayores en los hombres que en las mujeres (p < 0.05). Los valores de rendimiento de los algoritmos de aprendizaje automático fueron los siguientes: LDA 0.82, k-NN 0.80, LR 0.84, GaussianNB 0.80, DT 0.82 y ANN 0.82. Conclusiones: Las medidas morfométricas del seno esfenoidal, analizadas con los algoritmos LDA, LR, DT y ANN, mostraron alta precisión y proporcionaron datos fiables para la predicción del sexo. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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