Facial morphology analysis in osteogenesis imperfecta types I, III and IV using computer vision



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Rousseau, Maxime and Vargas Balbuena, Javier and Rauch, Frank and Marulanda, Juliana and Retrouvey, Jean Marc (2021) Facial morphology analysis in osteogenesis imperfecta types I, III and IV using computer vision. Orthodontics & craniofacial research . ISSN 1601-6335

[thumbnail of Vargas, Javier 02 Postprint+EMB 10-may-2022.pdf]

Official URL: http://dx.doi.org/10.1111/ocr.12491


Objective Osteogenesis imperfecta (OI) is an autosomal dominant genetic disease that mainly affects the COL1A1/A2 genes. Individuals affected by OI types I, III, and IV have been reported to demonstrate characteristic facial manifestations of the disease. This study aimed to quantitatively assess OI patients' morphological characteristics. Materials and Methods This retrospective case-control study involved 306 individuals (145 male and 161 female). It used automatic facial annotation and statistical shape analysis to compare facial photographs of individuals affected by OI types I, III, and IV with a normocephalic control group. Four facial ratios were used to compare facial proportions. Additionally, we proposed a novel approach to facial analysis using 68 landmarks and statistical shape analysis to compare morphological features. A predictive model (PCALog) was trained to detect whether a subject was affected by OI, based on facial landmarks. Results Our findings correlate with previous reports of OI type III patients' facial characteristics being the most severely affected among the three types studied. Our novel approach facilitated an interpretation and comparison of morphological changes. Moreover, we successfully trained our PCALog model to automatically detect OI based on landmark features. Conclusion We found patients' facial manifestations of OI to be more pronounced at the level of the eyes and temples. Our morphological approach facilitates the comparison of various groups and should be considered for future craniofacial analysis studies. Machine learning models can be trained using facial landmarks to detect the presence of conditions that affect facial morphology.

Item Type:Article
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© 2021 John Wiley & Sons A/S.
The Brittle Bone Disease Consortium (1U54AR068069-0) is part of the National Center for Advancing Translational Sciences (NCATS) at the Rare Diseases Clinical Research Network (RDCRN), and it is funded through a collaboration between the Office of Rare Diseases Research (ORDR), NCATS, the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Institute of Dental and Craniofacial Research (NIDCR), and the Eunice Kennedy Shriver National Institutes of Child Health and Development (NICHD). This article's authors are solely responsible for its content, and this article does not necessarily represent the National Institutes of Health's official views. The Brittle Bone Disease Consortium is also supported by the Osteogenesis Imperfecta Foundation. The authors also acknowledge the contributions of the following BBDC site coordinators: M. Abrahamson (OHSU), S. Alon (UCLA), M. Azamian and A. Turner (BCM), C. Brown (Nemours Alfred I. duPont Hospital), E. Carter and E. Yonko (HSS), A. Caudill (Chicago Shriners), K. Dobose (KKI), M. Durigova (Montreal Shriners Hospital for Children), A. Giles and E. Rajah (CNMC), M. Gross-King (Tampa Shriners), and E. Strudthoff (UNMC).

Uncontrolled Keywords:Children
Subjects:Sciences > Physics > Optics
ID Code:67209
Deposited On:31 Aug 2021 12:04
Last Modified:10 May 2022 22:00

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