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Diagnostic performance of convolutional neural networks for dental sexual dimorphism

Convolutional neural networks (CNN) led to important solutions in the field of Computer Vision. More recently, forensic sciences benefited from the resources of artificial intelligence, especially in procedures that normally require operator-dependent steps. Forensic tools for sexual dimorphism base...

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Autores principales: Franco, Ademir, Porto, Lucas, Heng, Dennis, Murray, Jared, Lygate, Anna, Franco, Raquel, Bueno, Juliano, Sobania, Marilia, Costa, Márcio M., Paranhos, Luiz R., Manica, Scheila, Abade, André
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9568558/
https://www.ncbi.nlm.nih.gov/pubmed/36241670
http://dx.doi.org/10.1038/s41598-022-21294-1
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author Franco, Ademir
Porto, Lucas
Heng, Dennis
Murray, Jared
Lygate, Anna
Franco, Raquel
Bueno, Juliano
Sobania, Marilia
Costa, Márcio M.
Paranhos, Luiz R.
Manica, Scheila
Abade, André
author_facet Franco, Ademir
Porto, Lucas
Heng, Dennis
Murray, Jared
Lygate, Anna
Franco, Raquel
Bueno, Juliano
Sobania, Marilia
Costa, Márcio M.
Paranhos, Luiz R.
Manica, Scheila
Abade, André
author_sort Franco, Ademir
collection PubMed
description Convolutional neural networks (CNN) led to important solutions in the field of Computer Vision. More recently, forensic sciences benefited from the resources of artificial intelligence, especially in procedures that normally require operator-dependent steps. Forensic tools for sexual dimorphism based on morphological dental traits are available but have limited performance. This study aimed to test the application of a machine learning setup to distinguish females and males using dentomaxillofacial features from a radiographic dataset. The sample consisted of panoramic radiographs (n = 4003) of individuals in the age interval of 6 and 22.9 years. Image annotation was performed with V7 software (V7labs, London, UK). From Scratch (FS) and Transfer Learning (TL) CNN architectures were compared, and diagnostic accuracy tests were used. TL (82%) performed better than FS (71%). The correct classifications of females and males aged ≥ 15 years were 87% and 84%, respectively. For females and males < 15 years, the correct classifications were 80% and 83%, respectively. The Area Under the Curve (AUC) from Receiver-operating Characteristic (ROC) curves showed high classification accuracy between 0.87 and 0.91. The radio-diagnostic use of CNN for sexual dimorphism showed positive outcomes and promising forensic applications to the field of dental human identification.
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spelling pubmed-95685582022-10-16 Diagnostic performance of convolutional neural networks for dental sexual dimorphism Franco, Ademir Porto, Lucas Heng, Dennis Murray, Jared Lygate, Anna Franco, Raquel Bueno, Juliano Sobania, Marilia Costa, Márcio M. Paranhos, Luiz R. Manica, Scheila Abade, André Sci Rep Article Convolutional neural networks (CNN) led to important solutions in the field of Computer Vision. More recently, forensic sciences benefited from the resources of artificial intelligence, especially in procedures that normally require operator-dependent steps. Forensic tools for sexual dimorphism based on morphological dental traits are available but have limited performance. This study aimed to test the application of a machine learning setup to distinguish females and males using dentomaxillofacial features from a radiographic dataset. The sample consisted of panoramic radiographs (n = 4003) of individuals in the age interval of 6 and 22.9 years. Image annotation was performed with V7 software (V7labs, London, UK). From Scratch (FS) and Transfer Learning (TL) CNN architectures were compared, and diagnostic accuracy tests were used. TL (82%) performed better than FS (71%). The correct classifications of females and males aged ≥ 15 years were 87% and 84%, respectively. For females and males < 15 years, the correct classifications were 80% and 83%, respectively. The Area Under the Curve (AUC) from Receiver-operating Characteristic (ROC) curves showed high classification accuracy between 0.87 and 0.91. The radio-diagnostic use of CNN for sexual dimorphism showed positive outcomes and promising forensic applications to the field of dental human identification. Nature Publishing Group UK 2022-10-14 /pmc/articles/PMC9568558/ /pubmed/36241670 http://dx.doi.org/10.1038/s41598-022-21294-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Franco, Ademir
Porto, Lucas
Heng, Dennis
Murray, Jared
Lygate, Anna
Franco, Raquel
Bueno, Juliano
Sobania, Marilia
Costa, Márcio M.
Paranhos, Luiz R.
Manica, Scheila
Abade, André
Diagnostic performance of convolutional neural networks for dental sexual dimorphism
title Diagnostic performance of convolutional neural networks for dental sexual dimorphism
title_full Diagnostic performance of convolutional neural networks for dental sexual dimorphism
title_fullStr Diagnostic performance of convolutional neural networks for dental sexual dimorphism
title_full_unstemmed Diagnostic performance of convolutional neural networks for dental sexual dimorphism
title_short Diagnostic performance of convolutional neural networks for dental sexual dimorphism
title_sort diagnostic performance of convolutional neural networks for dental sexual dimorphism
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9568558/
https://www.ncbi.nlm.nih.gov/pubmed/36241670
http://dx.doi.org/10.1038/s41598-022-21294-1
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