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Gender estimation using machine learning algorithms and artificial neural networks based on parameters obtained from the sphenoid sinus.

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Title: Gender estimation using machine learning algorithms and artificial neural networks based on parameters obtained from the sphenoid sinus.
Alternate Title: Predicción del sexo usando algoritmos de aprendizaje automático y redes neuronales artificiales a partir de parámetros obtenidos del seno esfenoidal.
Authors: Harmandaoglu, Oguzhan1 (AUTHOR), Secgin, Yusuf2 (AUTHOR), Kaya, Seren3 (AUTHOR), Senol, Deniz3 (AUTHOR) denizanatomi@gmail.com, Oner, Zulal4 (AUTHOR), Onbas, Omer5 (AUTHOR)
Source: Cirugía y Cirujanos. jan/feb2026, Vol. 94 Issue 1, p93-99. 7p.
Subjects: MACHINE learning, ARTIFICIAL neural networks, MORPHOMETRICS, CLASSIFICATION algorithms, SPHENOID sinus, DIAGNOSTIC sex determination, COMPUTED tomography
Abstract (English): 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. [ABSTRACT FROM AUTHOR]
Abstract (Spanish): 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. [ABSTRACT FROM AUTHOR]
Copyright of Cirugía y Cirujanos is the property of Publicidad Permanyer SLU and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: MedicLatina
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  Data: Gender estimation using machine learning algorithms and artificial neural networks based on parameters obtained from the sphenoid sinus.
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  Data: Predicci&#243;n del sexo usando algoritmos de aprendizaje autom&#225;tico y redes neuronales artificiales a partir de par&#225;metros obtenidos del seno esfenoidal.
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  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Harmandaoglu%2C+Oguzhan%22&quot;&gt;Harmandaoglu, Oguzhan&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Secgin%2C+Yusuf%22&quot;&gt;Secgin, Yusuf&lt;/searchLink&gt;&lt;relatesTo&gt;2&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Kaya%2C+Seren%22&quot;&gt;Kaya, Seren&lt;/searchLink&gt;&lt;relatesTo&gt;3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Senol%2C+Deniz%22&quot;&gt;Senol, Deniz&lt;/searchLink&gt;&lt;relatesTo&gt;3&lt;/relatesTo&gt; (AUTHOR)&lt;i&gt; denizanatomi@gmail.com&lt;/i&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Oner%2C+Zulal%22&quot;&gt;Oner, Zulal&lt;/searchLink&gt;&lt;relatesTo&gt;4&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Onbas%2C+Omer%22&quot;&gt;Onbas, Omer&lt;/searchLink&gt;&lt;relatesTo&gt;5&lt;/relatesTo&gt; (AUTHOR)
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– Name: Abstract
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  Data: 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 &lt; 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Spanish)
  Group: Ab
  Data: Objetivo: Predecir el sexo mediante el uso de algoritmos de aprendizaje autom&#225;tico y redes neuronales artificiales a partir de par&#225;metros del seno esfenoidal medidos en im&#225;genes de tomograf&#237;a computarizada (TC). M&#233;todo: Se evaluaron las mediciones de longitud, ancho y volumen del seno esfenoidal de im&#225;genes de TC de 300 individuos, 150 hombres y 150 mujeres, con un rango de edad de 18 a 65 a&#241;os. Se utilizaron los algoritmos de an&#225;lisis discriminante lineal (LDA), an&#225;lisis discriminante cuadr&#225;tico (QDA), regresi&#243;n log&#237;stica (LR), clasificador de &#225;rbol extra (ETC), bosque aleatorio (RF), &#225;rbol de decisi&#243;n (DT), Gaussian Naive Bayes (GaussianNB), vecinos m&#225;s pr&#243;ximos (k-NN) y modelo de redes neuronales artificiales (ANN) para la predicci&#243;n del sexo. Resultados: Las longitudes, anchuras y vol&#250;menes del seno esfenoidal izquierdo y derecho fueron significativamente mayores en los hombres que en las mujeres (p &lt; 0.05). Los valores de rendimiento de los algoritmos de aprendizaje autom&#225;tico 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 morfom&#233;tricas del seno esfenoidal, analizadas con los algoritmos LDA, LR, DT y ANN, mostraron alta precisi&#243;n y proporcionaron datos fiables para la predicci&#243;n del sexo. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Cirug&#237;a y Cirujanos is the property of Publicidad Permanyer SLU and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.24875/CIRU.24000558
    Languages:
      – Code: eng
        Text: English
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        PageCount: 7
        StartPage: 93
    Subjects:
      – SubjectFull: MACHINE learning
        Type: general
      – SubjectFull: ARTIFICIAL neural networks
        Type: general
      – SubjectFull: MORPHOMETRICS
        Type: general
      – SubjectFull: CLASSIFICATION algorithms
        Type: general
      – SubjectFull: SPHENOID sinus
        Type: general
      – SubjectFull: DIAGNOSTIC sex determination
        Type: general
      – SubjectFull: COMPUTED tomography
        Type: general
    Titles:
      – TitleFull: Gender estimation using machine learning algorithms and artificial neural networks based on parameters obtained from the sphenoid sinus.
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            NameFull: Harmandaoglu, Oguzhan
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              M: 01
              Text: jan/feb2026
              Type: published
              Y: 2026
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