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Artificial neural network for gender determination using mandibular morphometric parameters: A comparative retrospective study
Manipal Acad Higher Educ, Manipal Coll Dent Sci, Dept Oral Med & Radiol, Manipal 576104, Karnataka, India..
Manipal Acad Higher Educ, Manipal Coll Dent Sci, Dept Oral Med & Radiol, Manipal 576104, Karnataka, India..
Manipal Acad Higher Educ, Manipal Coll Dent Sci, Dept Oral Med & Radiol, Manipal 576104, Karnataka, India..
Manipal Acad Higher Educ, Manipal Inst Technol, Dept Human & Management, Manipal 576104, Karnataka, India..
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2020 (English)In: Cogent Engineering, ISSN 2331-1916, Vol. 7, no 1, article id 1723783Article in journal (Refereed) Published
Abstract [en]

Gender determination is of paramount importance in order to identify the diseased in cases of mass disasters and accidents and to resolve all medico-legal 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 prediction tool that can be applied in the field of forensic sciences for near accurate results. Its application is promising as it automates and eases the method of identifying unknown gender or age with minimal errors.

Place, publisher, year, edition, pages
Taylor & Francis, 2020. Vol. 7, no 1, article id 1723783
Keywords [en]
Artificial intelligence, artificial neural network, mandible, gender classification, panoramic radiographs, forensic, dentistry
National Category
Clinical Medicine
Identifiers
URN: urn:nbn:se:kth:diva-268807DOI: 10.1080/23311916.2020.1723783ISI: 000511433500001Scopus ID: 2-s2.0-85079400361OAI: oai:DiVA.org:kth-268807DiVA, id: diva2:1395757
Note

QC 20200224

Available from: 2020-02-24 Created: 2020-02-24 Last updated: 2020-02-24Bibliographically approved

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Raju, Adithya

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