Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic Science
Background: Sex estimation has been extensively investigated due to its importance for forensic science. Several anatomical structures of the human body have been used for this process. The human skull has important landmarks that can serve as reliable sex estimation predictors. Methods: In this stu...
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2024-12-01
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| author | George Triantafyllou George G. Botis Maria Piagkou Konstantinos Papanastasiou George Tsakotos Ioannis Paschopoulos George K. Matsopoulos Stavroula Papadodima |
| author_facet | George Triantafyllou George G. Botis Maria Piagkou Konstantinos Papanastasiou George Tsakotos Ioannis Paschopoulos George K. Matsopoulos Stavroula Papadodima |
| author_sort | George Triantafyllou |
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| description | Background: Sex estimation has been extensively investigated due to its importance for forensic science. Several anatomical structures of the human body have been used for this process. The human skull has important landmarks that can serve as reliable sex estimation predictors. Methods: In this study, orbital measurements from 92 dried skulls, comprising 35 males and 57 females, were utilized to develop a machine-learning-based classifier for sex estimation with potential applications in forensic science. The parameters evaluated included optic foramen height (OFH), optic foramen width (OFW), optic canal height (OCH), optic canal width (OCW), intraorbital distance (IOD), extraorbital distance (EOD), orbit height (OH), and orbit width (OW). Results: A Random Forest classifier was employed to analyze the data, achieving an overall test accuracy of 0.68. The model demonstrated a precision of 0.65, indicating a moderate level of false positives. The recall was 0.70, reflecting that 70% of the positive cases were correctly identified. The F1 score was calculated at 0.675, suggesting a balanced performance between precision and recall. The area under the curve (ROC AUC) score was also 0.72, indicating that the model can distinguish between classes. The most important features in the best subset were OW (0.2429), IOD (0.2059), EOD (0.1927), OFH (0.1798), and OFW (0.1787), highlighting their significant contributions to the model’s predictions. Conclusions: These findings suggest that orbital measurements could potentially serve as reliable predictors for automated sex estimation, contributing to advancements in forensic identification techniques |
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| language | English |
| publishDate | 2024-12-01 |
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| series | Diagnostics |
| spelling | doaj-art-32ab204262a242d383e7f2ba87d650552024-12-27T14:20:41ZengMDPI AGDiagnostics2075-44182024-12-011424277310.3390/diagnostics14242773Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic ScienceGeorge Triantafyllou0George G. Botis1Maria Piagkou2Konstantinos Papanastasiou3George Tsakotos4Ioannis Paschopoulos5George K. Matsopoulos6Stavroula Papadodima7Department of Anatomy, School of Medicine, Faculty of Health Sciences, National and Kapodistrian University of Athens, 11 527 Goudi, GreeceDepartment of Anatomy, School of Medicine, Faculty of Health Sciences, National and Kapodistrian University of Athens, 11 527 Goudi, GreeceDepartment of Anatomy, School of Medicine, Faculty of Health Sciences, National and Kapodistrian University of Athens, 11 527 Goudi, GreeceBiomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15 773 Zografou, GreeceDepartment of Anatomy, School of Medicine, Faculty of Health Sciences, National and Kapodistrian University of Athens, 11 527 Goudi, GreeceDepartment of Anatomy, School of Medicine, Faculty of Health Sciences, National and Kapodistrian University of Athens, 11 527 Goudi, GreeceBiomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15 773 Zografou, GreeceDepartment of Forensic Medicine and Toxicology, School of Medicine, National and Kapodistrian University of Athens, 11 527 Goudi, GreeceBackground: Sex estimation has been extensively investigated due to its importance for forensic science. Several anatomical structures of the human body have been used for this process. The human skull has important landmarks that can serve as reliable sex estimation predictors. Methods: In this study, orbital measurements from 92 dried skulls, comprising 35 males and 57 females, were utilized to develop a machine-learning-based classifier for sex estimation with potential applications in forensic science. The parameters evaluated included optic foramen height (OFH), optic foramen width (OFW), optic canal height (OCH), optic canal width (OCW), intraorbital distance (IOD), extraorbital distance (EOD), orbit height (OH), and orbit width (OW). Results: A Random Forest classifier was employed to analyze the data, achieving an overall test accuracy of 0.68. The model demonstrated a precision of 0.65, indicating a moderate level of false positives. The recall was 0.70, reflecting that 70% of the positive cases were correctly identified. The F1 score was calculated at 0.675, suggesting a balanced performance between precision and recall. The area under the curve (ROC AUC) score was also 0.72, indicating that the model can distinguish between classes. The most important features in the best subset were OW (0.2429), IOD (0.2059), EOD (0.1927), OFH (0.1798), and OFW (0.1787), highlighting their significant contributions to the model’s predictions. Conclusions: These findings suggest that orbital measurements could potentially serve as reliable predictors for automated sex estimation, contributing to advancements in forensic identification techniqueshttps://www.mdpi.com/2075-4418/14/24/2773orbitoptic canaloptic foramenorbital measurementssex estimationmachine learning |
| spellingShingle | George Triantafyllou George G. Botis Maria Piagkou Konstantinos Papanastasiou George Tsakotos Ioannis Paschopoulos George K. Matsopoulos Stavroula Papadodima Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic Science Diagnostics orbit optic canal optic foramen orbital measurements sex estimation machine learning |
| title | Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic Science |
| title_full | Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic Science |
| title_fullStr | Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic Science |
| title_full_unstemmed | Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic Science |
| title_short | Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic Science |
| title_sort | sex estimation through orbital measurements a machine learning approach for forensic science |
| topic | orbit optic canal optic foramen orbital measurements sex estimation machine learning |
| url | https://www.mdpi.com/2075-4418/14/24/2773 |
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