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. |
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| 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] |
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| Database: | MedicLatina |
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