Predicting the anthropometric properties of cranial structures using big data
The objective of this study was to generate predictive statistical models of the anthropometric dimensions of craniofacial structures, from medical images obtained by Computed Tomography (CT). The study consisted of two-dimensional measurement of the distances between the anthropometric points Glabe...
- Autores:
-
Viloria, Amelec
Pinillos-Patiño, Yisel
Pineda, Omar
Romero Marin, Ligia Cielo
- 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:11323/7801
- Acceso en línea:
- https://hdl.handle.net/11323/7801
https://doi.org/10.1016/j.procs.2020.03.112
https://repositorio.cuc.edu.co/
- Palabra clave:
- ANOVA
Anthropometry
Medical Imaging
Computed Tomography
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Predicting the anthropometric properties of cranial structures using big data |
| title |
Predicting the anthropometric properties of cranial structures using big data |
| spellingShingle |
Predicting the anthropometric properties of cranial structures using big data ANOVA Anthropometry Medical Imaging Computed Tomography |
| title_short |
Predicting the anthropometric properties of cranial structures using big data |
| title_full |
Predicting the anthropometric properties of cranial structures using big data |
| title_fullStr |
Predicting the anthropometric properties of cranial structures using big data |
| title_full_unstemmed |
Predicting the anthropometric properties of cranial structures using big data |
| title_sort |
Predicting the anthropometric properties of cranial structures using big data |
| dc.creator.fl_str_mv |
Viloria, Amelec Pinillos-Patiño, Yisel Pineda, Omar Romero Marin, Ligia Cielo |
| dc.contributor.author.spa.fl_str_mv |
Viloria, Amelec Pinillos-Patiño, Yisel Pineda, Omar Romero Marin, Ligia Cielo |
| dc.subject.spa.fl_str_mv |
ANOVA Anthropometry Medical Imaging Computed Tomography |
| topic |
ANOVA Anthropometry Medical Imaging Computed Tomography |
| description |
The objective of this study was to generate predictive statistical models of the anthropometric dimensions of craniofacial structures, from medical images obtained by Computed Tomography (CT). The study consisted of two-dimensional measurement of the distances between the anthropometric points Glabella, Vertex, Eurion, Nasion and Opisthocranium to achieve the dimensions: skull length (G-Op), head width (Eu-Eu) and head height (V-N). The iQ-VIEW/ iQ-Lite software was used for measurement. A total of 30 adult skulls between the ages of 50 and 70 were measured, all inhabitants of the cityof Medellin, Colombia. The mean and standard deviation values were calculated. A predictive model was developed using multiple linear regression, which predicts the distance corresponding to head height (V-N) relative to G-Op and Eu-Eu regressors, obtaining a square R value of 0.375. Positive correlations were observed between the three craniofacial dimensions. |
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2020 |
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2020 |
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2021-01-29T19:04:22Z |
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2021-01-29T19:04:22Z |
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https://doi.org/10.1016/j.procs.2020.03.112 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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1 P. Ward Booth, B. Eppley, R. Schmelzeisen Maxillofacial Trauma and Esthetic Facial Reconstruction (2nd Edition), Saunders, St. Louis, Missouri (2016) 2 INEGI Estadistica a Proporsito del Día Mundial de la Diabetes, Día Mund. la Diabetes (2013), p. 18 3 T. Santhanam, M.S. Padmavathi Application of K-Means and genetic algorithms for dimension reduction by integrating SVM for diabetes diagnosis Procedia Comput. Sci., 47 (C) (2014), pp. 76-83 4 Viloria A., Lis-Gutiérrez J.P., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018) 5 C. Díaz, R. García, G. Santacruz, G. Aguilar Craneoplastía con implante de titanio individualizado mediante tecnología CAD/CAM Implantol Act, 12 (24) (2016), pp. 4-7 6 D.O. Visscher, E. Farré-Guasch, M.N. Helder, S. Gibbs, T. Forouzanfar, P.P. van Zuijlen, J. Wolff Advances in Bioprinting Technologies for Craniofacial Reconstruction Trends Biotechnol., 34 (9) (2016), pp. 700-710 doi: https://doi.org/10.1016/j.tibtech.2016.04.001 7 T. Teshima, V. Patel, J. Mainprize, G. Edwards, O. Antonyshyn Three-Dimensional Statistical Average Skull: Application of Biometric Morphing in Generating Missing Anatomy J. Craniofac. Surg., 26 (5) (2015), pp. 1634-1638 doi: 10.1097 / SCS.0000000000001869. 8 K.V.S.R.P. Varma, A.A. Rao, T. Sita, Maha Lakshmi, P.V. Nageswara Rao A computational intelligence approach for a better diagnosis of diabetic patients Comput. Electr. Eng., 40 (5) (2014), pp. 1758-1765 9 K. Krishan Anthropometry in Forensic Medicine and Forensic Science-Forensic Anthropometry Int. J. Foren. Sci., 2 (1) (2007), pp. 1-8 10 R. Ward, P. Jamison Measurement precision and reliability in craniofacial anthropometry: implications and suggestions for clinical applications J Craniofac Genet Dev Biol., 11 (3) (1991), pp. 156-164 11 Mellado A., Suárez N., Altimir C., Martínez C., Pérez J.C., Krause M., Horvath A. Disentangling the change-alliance relationship: Observational assessment of the therapeutic alliance during change and stuck episodes Psychotherapy Research, 27 (5) (2017), pp. 595-607 doi: 10.1080/10503307.2016.1147657 12 Ogles B.M. Measuring change in psychotherapy research M.J. Lambert (Ed.), Bergin and Garfields’s Handbook of Psychotherapy and Behavior Change, Wiley, New Jersey (2013), pp. 134-166 13 El Pasante, «Ventajas y desventajas de las bases de datos,» 17 Junio 2015. [En línea]. Available: https://educacion.elpensante.com/ventajas-y-desventajas-de-las-bases-de-datos/. [Último acceso: 12 Noviembre 2018]. 14 Probability Formula, «Hypergeometric Distribution,» [En línea]. Available: http://www.probabilityformula.org/hypergeometric-distribution.html. [Último acceso: 16 Noviembre 2018]. 15 Skrondal A., Rabe-Hesketh S. Generalized latent variable modeling, Chapman & Hall/CRC, Boca Raton (2004) 16 Y. Hwang, K.H. Lee, B. Choi, K.S. Lee, H.Y. Lee, W.S. Sir Study on the Korean adult cranical capacity J. Korean Sci., 10 (1995), pp. 239-242 17 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 18 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 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. |
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Viloria, Amelecfc29d54ed3c7d39e34b3d61c512ace8fPinillos-Patiño, Yisel39fbde1446be4582728fbecfbc54a8eaPineda, Omaraf4b322b3d3157067b1e466da357fb98Romero Marin, Ligia Cielo4300794b3051ef176ce3fed4897c56942021-01-29T19:04:22Z2021-01-29T19:04:22Z2020https://hdl.handle.net/11323/7801https://doi.org/10.1016/j.procs.2020.03.112Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The objective of this study was to generate predictive statistical models of the anthropometric dimensions of craniofacial structures, from medical images obtained by Computed Tomography (CT). The study consisted of two-dimensional measurement of the distances between the anthropometric points Glabella, Vertex, Eurion, Nasion and Opisthocranium to achieve the dimensions: skull length (G-Op), head width (Eu-Eu) and head height (V-N). The iQ-VIEW/ iQ-Lite software was used for measurement. A total of 30 adult skulls between the ages of 50 and 70 were measured, all inhabitants of the cityof Medellin, Colombia. The mean and standard deviation values were calculated. A predictive model was developed using multiple linear regression, which predicts the distance corresponding to head height (V-N) relative to G-Op and Eu-Eu regressors, obtaining a square R value of 0.375. Positive correlations were observed between the three craniofacial dimensions.application/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/S1877050920305500#!ANOVAAnthropometryMedical ImagingComputed TomographyPredicting the anthropometric properties of cranial structures using big dataArtículo 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 P. Ward Booth, B. Eppley, R. Schmelzeisen Maxillofacial Trauma and Esthetic Facial Reconstruction (2nd Edition), Saunders, St. Louis, Missouri (2016)2 INEGI Estadistica a Proporsito del Día Mundial de la Diabetes, Día Mund. la Diabetes (2013), p. 183 T. Santhanam, M.S. Padmavathi Application of K-Means and genetic algorithms for dimension reduction by integrating SVM for diabetes diagnosis Procedia Comput. Sci., 47 (C) (2014), pp. 76-834 Viloria A., Lis-Gutiérrez J.P., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018)5 C. Díaz, R. García, G. Santacruz, G. Aguilar Craneoplastía con implante de titanio individualizado mediante tecnología CAD/CAM Implantol Act, 12 (24) (2016), pp. 4-76 D.O. Visscher, E. Farré-Guasch, M.N. Helder, S. Gibbs, T. Forouzanfar, P.P. van Zuijlen, J. Wolff Advances in Bioprinting Technologies for Craniofacial Reconstruction Trends Biotechnol., 34 (9) (2016), pp. 700-710 doi: https://doi.org/10.1016/j.tibtech.2016.04.0017 T. Teshima, V. Patel, J. Mainprize, G. Edwards, O. Antonyshyn Three-Dimensional Statistical Average Skull: Application of Biometric Morphing in Generating Missing Anatomy J. Craniofac. Surg., 26 (5) (2015), pp. 1634-1638 doi: 10.1097 / SCS.0000000000001869.8 K.V.S.R.P. Varma, A.A. Rao, T. Sita, Maha Lakshmi, P.V. Nageswara Rao A computational intelligence approach for a better diagnosis of diabetic patients Comput. Electr. Eng., 40 (5) (2014), pp. 1758-17659 K. Krishan Anthropometry in Forensic Medicine and Forensic Science-Forensic Anthropometry Int. J. Foren. Sci., 2 (1) (2007), pp. 1-810 R. Ward, P. Jamison Measurement precision and reliability in craniofacial anthropometry: implications and suggestions for clinical applications J Craniofac Genet Dev Biol., 11 (3) (1991), pp. 156-16411 Mellado A., Suárez N., Altimir C., Martínez C., Pérez J.C., Krause M., Horvath A. Disentangling the change-alliance relationship: Observational assessment of the therapeutic alliance during change and stuck episodes Psychotherapy Research, 27 (5) (2017), pp. 595-607 doi: 10.1080/10503307.2016.114765712 Ogles B.M. Measuring change in psychotherapy research M.J. Lambert (Ed.), Bergin and Garfields’s Handbook of Psychotherapy and Behavior Change, Wiley, New Jersey (2013), pp. 134-16613 El Pasante, «Ventajas y desventajas de las bases de datos,» 17 Junio 2015. [En línea]. Available: https://educacion.elpensante.com/ventajas-y-desventajas-de-las-bases-de-datos/. [Último acceso: 12 Noviembre 2018].14 Probability Formula, «Hypergeometric Distribution,» [En línea]. Available: http://www.probabilityformula.org/hypergeometric-distribution.html. [Último acceso: 16 Noviembre 2018].15 Skrondal A., Rabe-Hesketh S. Generalized latent variable modeling, Chapman & Hall/CRC, Boca Raton (2004)16 Y. Hwang, K.H. Lee, B. Choi, K.S. Lee, H.Y. Lee, W.S. Sir Study on the Korean adult cranical capacity J. Korean Sci., 10 (1995), pp. 239-24217 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. 1-818 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, Cham19 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.ORIGINALPredicting the anthropometric properties of cranial structures using big data.pdfPredicting the anthropometric properties of cranial structures using big data.pdfapplication/pdf98416https://repositorio.cuc.edu.co/bitstream/11323/7801/1/Predicting%20the%20anthropometric%20properties%20of%20cranial%20structures%20using%20big%20data.pdfaa60ef249638893ab5a7362a7e4fd750MD51open accessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstream/11323/7801/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstream/11323/7801/3/license.txte30e9215131d99561d40d6b0abbe9badMD53open accessTHUMBNAILPredicting the anthropometric properties of cranial structures using big data.pdf.jpgPredicting the anthropometric properties of cranial structures using big 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