Objective: To investigate the relationship between perception of craniofacial deformity, geometric head features, and 3D head shape analyzed by statistical shape modeling (SSM).
Patients: A total of 18 unoperated patients with scaphocephaly (age = 5.2 ± 1.1m)-6 were followed-up after spring-assisted cranioplasty (SAC) (age = 9.6 ± 1.5m)-and 6 controls (age = 6.7 ± 2.5m).
Main Outcome Measures: 3D head shapes were retrieved from 3D scans or computed tomography (CTs). Various geometrical features were measured: anterior and posterior prominence, take-off angle, average anterior and posterior lateral and horizontal curvatures, cranial index (CI) (cranial width over length), and turricephaly index (TI) (cranial height over length). SSM and principal component analysis (PCA) described shape variability. All models were 3D printed; the perception of deformity was blindly scored by 9 surgeons and 1 radiologist in terms of frontal bossing (FB), occipital bulleting (OB), biparietal narrowing (BN), low posterior vertex (LPV), and overall head shape (OHS).
Results: A moderate correlation was found between FB and anterior prominence (r = 0.56, < .01) and take-off angle (r = - 0.57, < .01). OB correlated with average posterior lateral curvature (r = 0.43, < 0.01) similarly to BPN (r = 0.55, < .01) and LPV (r = 0.43, < .01). OHS showed strong correlation with CI (r = - 0.68, < .01) and TI (r = 0.63, < .01). SSM Mode 1 correlated with OHS (r = 0.66, < .01) while Mode 3 correlated with FB (r = - 0.58, < .01).
Conclusions: Esthetic cranial appearance in craniofacial patients is correlated to specific geometric parameters and could be estimated using automated methods such as SSM.
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http://dx.doi.org/10.1177/10556656221111307 | DOI Listing |
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Institute of Physics of the CAS, v.v.i., Cukrovarnická 10, 162 00 Prague 6, Czechia.
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View Article and Find Full Text PDFHeliyon
December 2024
Higher Institute for Applied Sciences and Technology (HIAST), Damascus, P.O.Box 31983, Syria.
The precision and safety of robotic applications rely on accurate robot models. Bayesian Neural Networks (BNNs) offer the capability to acquire intricate models and provide insights into inherent uncertainties. While recent studies have successfully employed machine learning to predict the Forward Geometric Model (FGM) of a 6-DOF (degrees of freedom) parallel manipulator, traditional methods lack predictive uncertainty estimation.
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