Personalized quantification of facial normality: a machine learning approach

Abstract What is a normal face? A fundamental task for the facial reconstructive surgeon is to answer that question as it pertains to any given individual. Accordingly, it would be important to be able to place the facial appearance of a patient with congenital or acquired deformity numerically alon...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Osman Boyaci, Erchin Serpedin, Mitchell A. Stotland
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e369a5c1f6dd489687f99131a7e0c4f6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e369a5c1f6dd489687f99131a7e0c4f6
record_format	dspace
spelling	oai:doaj.org-article:e369a5c1f6dd489687f99131a7e0c4f62021-12-02T12:33:44ZPersonalized quantification of facial normality: a machine learning approach10.1038/s41598-020-78180-x2045-2322https://doaj.org/article/e369a5c1f6dd489687f99131a7e0c4f62020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78180-xhttps://doaj.org/toc/2045-2322Abstract What is a normal face? A fundamental task for the facial reconstructive surgeon is to answer that question as it pertains to any given individual. Accordingly, it would be important to be able to place the facial appearance of a patient with congenital or acquired deformity numerically along their own continuum of normality, and to measure any surgical changes against such a personalized benchmark. This has not previously been possible. We have solved this problem by designing a computerized model that produces realistic, normalized versions of any given facial image, and objectively measures the perceptual distance between the raw and normalized facial image pair. The model is able to faithfully predict human scoring of facial normality. We believe this work represents a paradigm shift in the assessment of the human face, holding great promise for development as an objective tool for surgical planning, patient education, and as a means for clinical outcome measurement.Osman BoyaciErchin SerpedinMitchell A. StotlandNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-19 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Osman Boyaci Erchin Serpedin Mitchell A. Stotland Personalized quantification of facial normality: a machine learning approach
description	Abstract What is a normal face? A fundamental task for the facial reconstructive surgeon is to answer that question as it pertains to any given individual. Accordingly, it would be important to be able to place the facial appearance of a patient with congenital or acquired deformity numerically along their own continuum of normality, and to measure any surgical changes against such a personalized benchmark. This has not previously been possible. We have solved this problem by designing a computerized model that produces realistic, normalized versions of any given facial image, and objectively measures the perceptual distance between the raw and normalized facial image pair. The model is able to faithfully predict human scoring of facial normality. We believe this work represents a paradigm shift in the assessment of the human face, holding great promise for development as an objective tool for surgical planning, patient education, and as a means for clinical outcome measurement.
format	article
author	Osman Boyaci Erchin Serpedin Mitchell A. Stotland
author_facet	Osman Boyaci Erchin Serpedin Mitchell A. Stotland
author_sort	Osman Boyaci
title	Personalized quantification of facial normality: a machine learning approach
title_short	Personalized quantification of facial normality: a machine learning approach
title_full	Personalized quantification of facial normality: a machine learning approach
title_fullStr	Personalized quantification of facial normality: a machine learning approach
title_full_unstemmed	Personalized quantification of facial normality: a machine learning approach
title_sort	personalized quantification of facial normality: a machine learning approach
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/e369a5c1f6dd489687f99131a7e0c4f6
work_keys_str_mv	AT osmanboyaci personalizedquantificationoffacialnormalityamachinelearningapproach AT erchinserpedin personalizedquantificationoffacialnormalityamachinelearningapproach AT mitchellastotland personalizedquantificationoffacialnormalityamachinelearningapproach
_version_	1718393891758014464

Personalized quantification of facial normality: a machine learning approach

Ejemplares similares