Artificial Neural Network as a Predictive Tool for Gender Determination using Volumetric and Linear Measurements of Maxillary Sinus CBCT: An Observational Study on South Indian Population

Abstract

Introduction: Determination of age and gender using bones of skull is central aspect of forensic odontology. Maxillary sinuses in this regard have shown high accuracy in predicting gender. Aim: To identify gender using the volumetric and linear measurements of maxillary sinuses obtained from a Cone Beam Computed Tomography (CBCT) by using Artificial Neural Network (ANN) based tool. Materials and Methods: A retrospective study was conducted on 80 volumes of CBCT (derived from n=80 patients) with equal gender distribution. The CBCT images were analysed for eight linear and two volumetric measurements namely the maxillary sinus height, maxillary sinus length, maxillary sinus width, distance between infraorbital foramen and distance between maxillary sinus. The data from these parameters were reported by two experts and subjected to discriminant analysis and McNemars test for gender determination. The same data was also fed to the ANN software and the accuracy of its gender prediction was analysed by Receiver Operating Characteristics Curve (ROC) and Area Under the Curve (AUC). Results: The ROC test and AUC (ANN –test) had shown high accuracy for prediction of gender form the data of the CBCT parameters for maxillary sinuses. McNemars test showed that the difference in proportion between the actual gender and ANN predicted gender was not significant (p-value=0.687) and the agreement between the actual gender and ANN in measuring the gender was 84.6%. The male sex was predicted correctly upto 89.7% and female sex upto 94.9%. Conclusion: This study has found ANN to have an encouraging predictive power in gender determination based on linear and volumetric measurements of maxillary sinus obtained from CBCT