Prediction of mandibular morphology through artificial neural networks

Prediction models are used for knowing the behavior of highly related complex data. The prediction of morphological structures, and especially the mandible from cranio-maxillary variables, has clinical and investigative odontological usefulness. For example, in cases of trauma, pathologies and in fo...

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Autores:
Viloria, Amelec
Mendinueta, Martha
Borrero, Luz Adriana
Pineda, Omar
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7803
Acceso en línea:
https://hdl.handle.net/11323/7803
https://doi.org/10.1016/j.procs.2020.03.064
https://repositorio.cuc.edu.co/
Palabra clave:
Artificial Neural Networks
Mandibular Bone
Prediction
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_d3a39ce6da8efdf31270d5186e21ece2
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7803
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Prediction of mandibular morphology through artificial neural networks
title Prediction of mandibular morphology through artificial neural networks
spellingShingle Prediction of mandibular morphology through artificial neural networks
Artificial Neural Networks
Mandibular Bone
Prediction
title_short Prediction of mandibular morphology through artificial neural networks
title_full Prediction of mandibular morphology through artificial neural networks
title_fullStr Prediction of mandibular morphology through artificial neural networks
title_full_unstemmed Prediction of mandibular morphology through artificial neural networks
title_sort Prediction of mandibular morphology through artificial neural networks
dc.creator.fl_str_mv Viloria, Amelec
Mendinueta, Martha
Borrero, Luz Adriana
Pineda, Omar
dc.contributor.author.spa.fl_str_mv Viloria, Amelec
Mendinueta, Martha
Borrero, Luz Adriana
Pineda, Omar
dc.subject.spa.fl_str_mv Artificial Neural Networks
Mandibular Bone
Prediction
topic Artificial Neural Networks
Mandibular Bone
Prediction
description Prediction models are used for knowing the behavior of highly related complex data. The prediction of morphological structures, and especially the mandible from cranio-maxillary variables, has clinical and investigative odontological usefulness. For example, in cases of trauma, pathologies and in forensic sciences, especially when it is necessary to¬ individualize a missing person, using facial reconstruction. The aim of this paper is to predict mandibular morphology through artificial neuronal networks, using cranio-maxillary measures in posterior-anterior radiographs.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-01-29T21:00:40Z
dc.date.available.none.fl_str_mv 2021-01-29T21:00:40Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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url https://hdl.handle.net/11323/7803
https://doi.org/10.1016/j.procs.2020.03.064
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identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1 Infante Contreras C, López LA. Uso de técnicas mul¬tivariadas para la clasificación de estructuras óseas craneanas: una aplicación en medicina forense. Bogotá, Colombia: Universidad Nacional de Colombia; 2003.
2 Guevara S, Infante-Contreras C, González FA. Uso de redes neuronales en la predicción de la morfología mandibular: aplicación forense. Bogotá, Colombia: Universidad Nacional de Colombia; 2006.
3 Sanggarnjanavanich S, Sekiya T, Nomura Y, Nakayama T, Hanada N, Nakamura Y Cranial-base morphology in adults with skeletal Class III malocclusion Am J Orthod Dentofacial Orthoped., 146 (1) (2014), pp. 82-91 Jul
4 Lu CH, Ko EW, Liu L Improving the video imaging pre¬diction of postsurgical facial profiles with an artificial neural network J Dent Sci., 4 (3) (2009), pp. 118-129 Sep
5 Hou T, Wang J, Li Y ADME Evaluation in Drug Discovery. 8. The Prediction of Human Intestinal Absorption by a Support Vector Machine Journal of Chemical Information and Modeling, 47 (6) (2007), pp. 2408-2415 Nov
6 International Multimedia Resource Center, «RAM vs. Hard Drive Memory, » 2018. [En línea). Available: https://www.lehigh.edu/~inimr/computer-basics- tutorial/ramvsdiskspacehtm.htm. [Último acceso: 13 noviembre 2018).
7 Kanehisa Laboratories, «KEGG: Kyoto Encyclopedia of Genes and Genome,» 2018. [En línea). Available: https://www.genome.jp/kegg/. [Último acceso: 25 07 2018).
8 United States Environmental Protection Agency, Appendix F. SMILES Notation Tutorial, Washington D.C., 2017.
9 United States Environmental Protection Agency, «SMILES Tutorial,» 21 febrero 2016. [En línea). Available: https://archive.epa.gov/med/med_archive_03/web/html/smiles.html. [Último acceso: 26 Julio 2018).
10 Daylight Chemical Information Systems, «4. SMARTS - A Language for Describing Molecular Patterns, » 2008. [En línea). Available: http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html. [Último acceso: 26 Julio 2018).
11 Lantz B. Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications. Birmingham: Packt Publ; 2013.
12 Maltarollo VG, Gertrudes JC, Oliveira PR, Honorio KM Applying machine learning techniques for ADME-Tox prediction: a review Expert Opinion on Drug Metabolism & Toxicology., 11 (2) (2015), pp. 259-271 Feb;
13 Shen J, Cheng F, Xu Y, Li W, Tang Y Estimation of ADME Properties with Substructure Pattern Recognition Journal of Chemical Information and Modeling., 50 (6) (2010), pp. 1034-1041 Jun 28
14 Bucci, N., Luna, M., Viloria, A., García, J.H., Parody, A., Varela, N., & López, L.A.B. [2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data [pp. 149-158). Springer, Cham.
15 Kyoto Encyclopedia of Genes and Genomes, «KEGG Release Notes, » [En línea). Available: https://www.kegg.jp/kegg/docs/relnote.html. [Último acceso: 10 octubre 2018).
16 Kyoto Encyclopedia of Genes and Genomes, «KEGG release history, » 2018. [En línea). Available: https://www.genome.jp/kegg/docs/upd_all.html. [Último acceso: 17 octubre 2018).
17 M. Linderman, J. Sorenson, L. Lee, G. Nolan Computational solutions to large-scale data management and analysis Nature Reviews Genetics, 11 (2010), pp. 647-657
18 L. Wang, X. Qung Xie Computational target fishing: what should chemogenomics researchers expect for the future of in silico drug design and discovery? Future Med Chem, 6 (3) (2014), pp. 247-249
19 Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J.P., Parody, A., Bent, D.E.S., & López, L.A.B. [2018, June). Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In International Conference on Data Mining and Big Data [pp. 304-313). Springer, Cham.
20 J. Swamidass†, P. Baldi Mathematical Correction for Fingerprint Similarity Measures to Improve Chemical Retrieval Journal of Chemical Information and Modeling, 47 (1) (2006), pp. 952-964
21 S. Arif, J. Holliday, P. Willett Comparison of chemical similarity measures using different numbers of query structures Journal of Information Science, 39 (1) (2013), pp. 1-8
22 Equipo Colombiano Interdisciplinario de Trabajo Fo¬rense y Asistencia Psicosocial. Apreciaciones a las exhumaciones e investigaciones forenses realizadas por la Unidad Nacional de Justicia y Paz de la Fisca¬lía General de la Nación. Bogotá, Colombia: Fiscalía General de la Nación; 2006.
23 Morales V, Martínez WA, Molano CP, Novoa NA, González CM, Pineda MT, et al. Informe de rendición de cuentas a los ciudadanos año 2011 Fiscalía General de la Nación, Imprenta Nacional, Bogotá, Colombia (2012)
24 Bilge Y, Kedici PS, Alakoç YD, Ülküer KÜ, Ilkyaz YY The identification of a dismembered human body: a multidisciplinary approach Forensic Sci Int., 137 (2-3) (2003), pp. 141-146 Nov
25 Benazzi S, Fantini M, De Crescenzio F, Mallegni G, Mallegni F, Persiani F, Gruppioni F The face of the poet Dante Alighieri reconstructed by virtual modelling and forensic anthropology techniques J Archaeol Sci., 36 (2) (2009), pp. 278-283 Feb
26 Wei JT, Zhang Z, Barnhill SD, Madyastha KR, Zhang H, Oesterling JE Understanding artificial neural net¬works and exploring their potential applications for the practicing urologist Urol., 52 (2) (1998), pp. 161-172 Aug
27 Resino S, Seoane JA, Bellon JM, Dorado J, Martin- Sanchez F, Alvarez E, Cosín J, López JC, López G, Miralles P, Berenguer J An artificial neural network improves the non-invasive diagnosis of significant fibrosis in HIV/HCV coinfected patients J Infect., 62 (1) (2011), pp. 77-86 Jan
28 Bloedorn E, Mani I Using NLP for machine learning of user profiles Intell Data Anal., 2 (1-4) (1998), pp. 3-18 Jan
29 Gamero, W.M., Ramírez, M.C., Parody, A., Viloria, A., López, M.H.A., & Kamatkar, S.J. (2018, June). Concentrations and size distributions of fungal bioaerosols in a municipal landfill. In International Conference on Data Mining and Big Data (pp. 244-253). Springer, Cham.
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spelling Viloria, AmelecMendinueta, MarthaBorrero, Luz AdrianaPineda, Omar2021-01-29T21:00:40Z2021-01-29T21:00:40Z2020https://hdl.handle.net/11323/7803https://doi.org/10.1016/j.procs.2020.03.064Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Prediction models are used for knowing the behavior of highly related complex data. The prediction of morphological structures, and especially the mandible from cranio-maxillary variables, has clinical and investigative odontological usefulness. For example, in cases of trauma, pathologies and in forensic sciences, especially when it is necessary to¬ individualize a missing person, using facial reconstruction. The aim of this paper is to predict mandibular morphology through artificial neuronal networks, using cranio-maxillary measures in posterior-anterior radiographs.Viloria, AmelecMendinueta, Martha-will be generated-orcid-0000-0002-0238-1551-600Borrero, Luz AdrianaPineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920305019#!Artificial Neural NetworksMandibular BonePredictionPrediction of mandibular morphology through artificial neural networksArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1 Infante Contreras C, López LA. Uso de técnicas mul¬tivariadas para la clasificación de estructuras óseas craneanas: una aplicación en medicina forense. Bogotá, Colombia: Universidad Nacional de Colombia; 2003.2 Guevara S, Infante-Contreras C, González FA. Uso de redes neuronales en la predicción de la morfología mandibular: aplicación forense. Bogotá, Colombia: Universidad Nacional de Colombia; 2006.3 Sanggarnjanavanich S, Sekiya T, Nomura Y, Nakayama T, Hanada N, Nakamura Y Cranial-base morphology in adults with skeletal Class III malocclusion Am J Orthod Dentofacial Orthoped., 146 (1) (2014), pp. 82-91 Jul4 Lu CH, Ko EW, Liu L Improving the video imaging pre¬diction of postsurgical facial profiles with an artificial neural network J Dent Sci., 4 (3) (2009), pp. 118-129 Sep5 Hou T, Wang J, Li Y ADME Evaluation in Drug Discovery. 8. The Prediction of Human Intestinal Absorption by a Support Vector Machine Journal of Chemical Information and Modeling, 47 (6) (2007), pp. 2408-2415 Nov6 International Multimedia Resource Center, «RAM vs. Hard Drive Memory, » 2018. [En línea). Available: https://www.lehigh.edu/~inimr/computer-basics- tutorial/ramvsdiskspacehtm.htm. [Último acceso: 13 noviembre 2018).7 Kanehisa Laboratories, «KEGG: Kyoto Encyclopedia of Genes and Genome,» 2018. [En línea). Available: https://www.genome.jp/kegg/. [Último acceso: 25 07 2018).8 United States Environmental Protection Agency, Appendix F. SMILES Notation Tutorial, Washington D.C., 2017.9 United States Environmental Protection Agency, «SMILES Tutorial,» 21 febrero 2016. [En línea). Available: https://archive.epa.gov/med/med_archive_03/web/html/smiles.html. [Último acceso: 26 Julio 2018).10 Daylight Chemical Information Systems, «4. SMARTS - A Language for Describing Molecular Patterns, » 2008. [En línea). Available: http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html. [Último acceso: 26 Julio 2018).11 Lantz B. Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications. Birmingham: Packt Publ; 2013.12 Maltarollo VG, Gertrudes JC, Oliveira PR, Honorio KM Applying machine learning techniques for ADME-Tox prediction: a review Expert Opinion on Drug Metabolism & Toxicology., 11 (2) (2015), pp. 259-271 Feb;13 Shen J, Cheng F, Xu Y, Li W, Tang Y Estimation of ADME Properties with Substructure Pattern Recognition Journal of Chemical Information and Modeling., 50 (6) (2010), pp. 1034-1041 Jun 2814 Bucci, N., Luna, M., Viloria, A., García, J.H., Parody, A., Varela, N., & López, L.A.B. [2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data [pp. 149-158). Springer, Cham.15 Kyoto Encyclopedia of Genes and Genomes, «KEGG Release Notes, » [En línea). Available: https://www.kegg.jp/kegg/docs/relnote.html. [Último acceso: 10 octubre 2018).16 Kyoto Encyclopedia of Genes and Genomes, «KEGG release history, » 2018. [En línea). Available: https://www.genome.jp/kegg/docs/upd_all.html. [Último acceso: 17 octubre 2018).17 M. Linderman, J. Sorenson, L. Lee, G. Nolan Computational solutions to large-scale data management and analysis Nature Reviews Genetics, 11 (2010), pp. 647-65718 L. Wang, X. Qung Xie Computational target fishing: what should chemogenomics researchers expect for the future of in silico drug design and discovery? Future Med Chem, 6 (3) (2014), pp. 247-24919 Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J.P., Parody, A., Bent, D.E.S., & López, L.A.B. [2018, June). Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In International Conference on Data Mining and Big Data [pp. 304-313). Springer, Cham.20 J. Swamidass†, P. Baldi Mathematical Correction for Fingerprint Similarity Measures to Improve Chemical Retrieval Journal of Chemical Information and Modeling, 47 (1) (2006), pp. 952-96421 S. Arif, J. Holliday, P. Willett Comparison of chemical similarity measures using different numbers of query structures Journal of Information Science, 39 (1) (2013), pp. 1-822 Equipo Colombiano Interdisciplinario de Trabajo Fo¬rense y Asistencia Psicosocial. Apreciaciones a las exhumaciones e investigaciones forenses realizadas por la Unidad Nacional de Justicia y Paz de la Fisca¬lía General de la Nación. Bogotá, Colombia: Fiscalía General de la Nación; 2006.23 Morales V, Martínez WA, Molano CP, Novoa NA, González CM, Pineda MT, et al. Informe de rendición de cuentas a los ciudadanos año 2011 Fiscalía General de la Nación, Imprenta Nacional, Bogotá, Colombia (2012)24 Bilge Y, Kedici PS, Alakoç YD, Ülküer KÜ, Ilkyaz YY The identification of a dismembered human body: a multidisciplinary approach Forensic Sci Int., 137 (2-3) (2003), pp. 141-146 Nov25 Benazzi S, Fantini M, De Crescenzio F, Mallegni G, Mallegni F, Persiani F, Gruppioni F The face of the poet Dante Alighieri reconstructed by virtual modelling and forensic anthropology techniques J Archaeol Sci., 36 (2) (2009), pp. 278-283 Feb26 Wei JT, Zhang Z, Barnhill SD, Madyastha KR, Zhang H, Oesterling JE Understanding artificial neural net¬works and exploring their potential applications for the practicing urologist Urol., 52 (2) (1998), pp. 161-172 Aug27 Resino S, Seoane JA, Bellon JM, Dorado J, Martin- Sanchez F, Alvarez E, Cosín J, López JC, López G, Miralles P, Berenguer J An artificial neural network improves the non-invasive diagnosis of significant fibrosis in HIV/HCV coinfected patients J Infect., 62 (1) (2011), pp. 77-86 Jan28 Bloedorn E, Mani I Using NLP for machine learning of user profiles Intell Data Anal., 2 (1-4) (1998), pp. 3-18 Jan29 Gamero, W.M., Ramírez, M.C., Parody, A., Viloria, A., López, M.H.A., & Kamatkar, S.J. (2018, June). Concentrations and size distributions of fungal bioaerosols in a municipal landfill. In International Conference on Data Mining and Big Data (pp. 244-253). 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