University of Oulu

Farnell, D. J. J., Richmond, S., Galloway, J., Zhurov, A. I., Pirttiniemi, P., Heikkinen, T., Harila, V., Matthews, H., & Claes, P. (2021). An exploration of adolescent facial shape changes with age via multilevel partial least squares regression. Computer Methods and Programs in Biomedicine, 200, 105935.

An exploration of adolescent facial shape changes with age via multilevel partial least squares regression

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Author: Farnell, D.J.J.1; Richmond, S.1; Galloway, J.1;
Organizations: 1School of Dentistry, Cardiff University, Heath Park, Cardiff CF14 4XY, United Kingdom
2Research Unit of Oral Health Sciences, Faculty of Medicine, University of Oulu, Oulu, Finland
3Medical Research Center Oulu (MRC Oulu), Oulu University Hospital, Oulu, Finland
4Medical Imaging Research Center, UZ Leuven, 3000 Leuven, Belgium
5Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium
6Facial Sciences Research Group, Murdoch Children's Research Institute, Melbourne
7Department of Paediatrics, University of Melbourne, Melbourne, Australia
8Department of Electrical Engineering, ESAT/PSI, KU Leuven, 3000 Leuven, Belgium
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link:
Language: English
Published: Elsevier, 2021
Publish Date: 2023-03-02


Background and Objectives: Multilevel statistical models represent the existence of hierarchies or clustering within populations of subjects (or shapes in this work). This is a distinct advantage over single-level methods that do not. Multilevel partial-least squares regression (mPLSR) is used here to study facial shape changes with age during adolescence in Welsh and Finnish samples comprising males and females.

Methods: 3D facial images were obtained for Welsh and Finnish male and female subjects at multiple ages from 12 to 17 years old. 1000 3D points were defined regularly for each shape by using “meshmonk” software. A three-level model was used here, including level 1 (sex/ethnicity); level 2, all “subject” variations excluding sex, ethnicity, and age; and level 3, age. The mathematical formalism of mPLSR is given in an Appendix.

Results: Differences in facial shape between the ages of 12 and 17 predicted by mPLSR agree well with previous results of multilevel principal components analysis (mPCA); buccal fat is reduced with increasing age and features such as the nose, brow, and chin become larger and more distinct. Differences due to ethnicity and sex are also observed. Plausible simulated faces are predicted from the model for different ages, sexes and ethnicities. Our models provide good representations of the shape data by consideration of appropriate measures of model fit (RMSE and R²).

Conclusions: Repeat measures in our dataset for the same subject at different ages can only be modelled indirectly at the lowest level of the model at discrete ages via mPCA. By contrast, mPLSR models age explicitly as a continuous covariate, which is a strong advantage of mPLSR over mPCA. These investigations demonstrate that multivariate multilevel methods such as mPLSR can be used to describe such age-related changes for dense 3D point data. mPLSR might be of much use in future for the prediction of facial shapes for missing persons at specific ages or for simulating shapes for syndromes that affect facial shape in new subject populations.

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Series: Computer methods and programs in biomedicine
ISSN: 0169-2607
ISSN-E: 1872-7565
ISSN-L: 0169-2607
Volume: 200
Article number: 105935
DOI: 10.1016/j.cmpb.2021.105935
Type of Publication: A1 Journal article – refereed
Field of Science: 313 Dentistry
Funding: This investigation was supported by the KU Leuven, BOF (C14/15/081), NIH (1-RO1-DE027023) and the FWO Flanders (G078518N).
Copyright information: © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license