학술논문
Structural equation modelling to estimate facial soft tissue thickness parameters based on ethnicity, gender and body mass index: a secondary study on an Iranian dataset.
Document Type
Academic Journal
Author
Nourmohammadi MJ; Student Research Committee, Lorestan University of Medical Sciences, Khorramabad, Iran.; Ahmadi SAY; Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Iran University of Medical Sciences, Tehran, Iran.; Rezaian J; Research Office for the History of Persian Medicine, Department of Anatomical Sciences, School of Medicine, Lorestan University of Medical Sciences, Khorramabad, Iran. jafarrezaian@gmail.com.
Source
Publisher: Springer International Country of Publication: Germany NLM ID: 8608029 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1279-8517 (Electronic) Linking ISSN: 09301038 NLM ISO Abbreviation: Surg Radiol Anat Subsets: MEDLINE
Subject
Language
English
Abstract
Purpose: According to the anthropological importance of soft facial tissue thickness parameters, we aimed to find the association of these parameters with Lur and Arab ethnicities, gender and body mass index (BMI). Structural equation modeling (SEM) was used.
Methods: As a secondary analysis, SEM was performed on a dataset of 100 participants. The participants were from Lur and Arab populations of Ahvaz province, Iran, from those who referred for magnetic resonance imaging (MRI) due to headache.
Results: Multivariate regression illustrated that mental eminence (ME), chain-lip fold (CLF) and end of nasals (END) could not be predicted by the independent variables (p > 0.05). Right masseteric region (RMST) had the maximum predictability with R2 = 0.365, followed by middle philtrum (MID) with R 2 = 0.358 (p < 0.001). With respect to our criterion to enter SEM, i.e. existing at least two significant covariates at significance level of 0.05, among staying parameters, only parameters of nasion (NA), MID, superior lip (SL), RMST and left masseteric region (LMST) remained. Among these cases, MID was the only parameter that its three covariates illustrated significant association.
Conclusion: MID parameter can be predicted by gender, BMI and Arab ethnicity. By carrying out such studies and creating database, such information can be used in plastic surgery, corpse identification, and facial reconstruction software in archeology.
(© 2023. The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.)
Methods: As a secondary analysis, SEM was performed on a dataset of 100 participants. The participants were from Lur and Arab populations of Ahvaz province, Iran, from those who referred for magnetic resonance imaging (MRI) due to headache.
Results: Multivariate regression illustrated that mental eminence (ME), chain-lip fold (CLF) and end of nasals (END) could not be predicted by the independent variables (p > 0.05). Right masseteric region (RMST) had the maximum predictability with R
Conclusion: MID parameter can be predicted by gender, BMI and Arab ethnicity. By carrying out such studies and creating database, such information can be used in plastic surgery, corpse identification, and facial reconstruction software in archeology.
(© 2023. The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.)