Literature DB >> 31326670

Prediction of nasal morphology in facial reconstruction: Validation and recalibration of the Rynn method.

Ozgur Bulut1, Ching-Yiu Jessica Liu2, Safa Gurcan3, Baki Hekimoglu4.   

Abstract

BACKGROUND: Prediction of the nose from the skull remains an important issue in forensic facial approximation. In 2010, Rynn et al. published a method of predicting nose projection from the skull. With this method, three craniometric measurements (x, y, z) are taken, and these are then used in regression formulae to estimate the nasal dimensions. AIM: The purpose of this study was to examine and test the accuracy of the Rynn et al. method and if necessary to adapt the formulae for this population. SUBJECTS AND METHODS: A sample of 90 CT scans of Turkish adults was used in the study. The actual and predicted dimensions were compared using t-test. The age of the individuals ranged from 20 to 40 years by sex.
RESULTS: The descriptive statistics and correlations were calculated, and the actual and predicted measurements were compared. The differences between the actual and predicted values were statistically significant (p < 0.01), with -1 mm for males and -1.5 mm for females. Validation accuracies ranged from 76 to 92% in females and 72 to 82% in males. Recalibration equation accuracies ranged from 88 to 100% in females and 90 to 100% in males.
CONCLUSION: The results showed that the recalibration of the Rynn et al. method and its formulae gave satisfactory results with less error and can be employed in facial approximation cases.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Facial approximation; Forensic anthropology; Nasal morphology prediction

Mesh:

Year:  2019        PMID: 31326670     DOI: 10.1016/j.legalmed.2019.07.002

Source DB:  PubMed          Journal:  Leg Med (Tokyo)        ISSN: 1344-6223            Impact factor:   1.376


  1 in total

1.  Craniofacial anthropometric investigation of relationships between the nose and nasal aperture using 3D computed tomography of Korean subjects.

Authors:  Joon Yeol Ryu; Ki-Su Park; Min-Ji Kim; Ji-Su Yun; U-Young Lee; Sang-Seob Lee; Byung-Yoon Roh; Jeong-Uk Seo; Chang-Un Choi; Won-Joon Lee
Journal:  Sci Rep       Date:  2020-09-30       Impact factor: 4.379

  1 in total

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