Artificial neural network for gender determination using mandibular morphometric parameters: A comparative retrospective study

Patil, Vatsala and Vineetha, Ravindranath and Vatsa, Soumya and Shetty, Dasharathraj K and Raju, Adithya and Naik, Nithesh and Namesh, Malarout (2020) Artificial neural network for gender determination using mandibular morphometric parameters: A comparative retrospective study. Cogent Engineering, 7. ISSN 2331-1916

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Abstract

Gender determination is of paramount importance in order to identify the diseased in cases of mass disasters and accidents and to resolve all medicolegal issues in cases of violence. Skeletal bones are the strongest bones in the body and they play a crucial role in identifying a person’s gender. ANN is a relatively new technology, is fast emerging as a better prediction model for gender when used with skeletal bones like the femur. Prior studies have extensively used discriminant analysis, logistic regression and other similar statistical tools to understand the role of the mandible and its efficacy in gender determination. This study uses Artificial Neural Networks (ANN) for gender determination and compares results thus obtained with logistic regression and discriminant analysis using mandibular parameters as inputs. Digital panoramic radiographs were used to measure the mandible of 509 individuals. Six linear parameters and one angular parameter of each individual were obtained. Logistic Regression, Discriminant Analysis, and ANN analysis were performed on these parameters. The discriminant analysis had an overall accuracy of 69.1%, logistic regression showed an accuracy of 69.9% and ANN exhibited a higher accuracy of 75%. The results revealed that ANN is a good gender

Item Type: Article
Uncontrolled Keywords: Artificial intelligence; artificial neural network; mandible; gender classification; panoramic radiographs; forensic; dentistry
Subjects: Dentistry > MCODS Manipal > Oral Medicine and Radiology
Engineering > MIT Manipal > Humanities and Management
Engineering > MIT Manipal > Mechanical and Manufacturing
Depositing User: MIT Library
Date Deposited: 07 May 2020 06:15
Last Modified: 07 May 2020 06:15
URI: http://eprints.manipal.edu/id/eprint/155098

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