Computer-Assisted Diagnosis of Genetic Syndromes Using 3D Facial Surface Scans
Date
2023-03-09
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Abstract
Due to the complexity and rarity of genetic syndromes, one of the primary difficulties in treating afflicted patients is diagnosing their condition. Gene technologies have been a key tool to improve diagnosis rates, but genetic testing remains inaccurate, inaccessible, or expensive for many people. Computer-assisted facial phenotyping is a complementary strategy that makes use of inexpensive and widely available technologies. Many genetic syndromes are known to be associated with altered facial morphology, and clinical geneticists often make use of facial phenotype to inform diagnoses. The overarching objective of this research was to develop clinically useful image processing algorithms and machine learning models to improve computer-assisted facial phenotyping and syndrome diagnosis systems based on 3D facial surface images. First, a fully automated 3D facial landmarking algorithm was developed to prepare 3D facial surface scans for analysis without manual labor. Next, analyses comparing different 2D and 3D facial representations were performed to determine an optimal facial image acquisition strategy. Machine learning models of 3D facial morphology were then developed to identify abnormal and characteristically syndromic faces. Additionally, an analysis of non-syndromic facial morphology was performed to present quantitative information about facial sex differences to facial surgeons. The main contributions of this thesis are the automated 3D scan processing methods and normalizing flow framework for 3D facial shape modelling that provide the computational methods needed to create a complete and highly interpretable 3D face-based computer aided diagnosis system. Additionally, results from the subject-matched analysis of 2D and 3D facial representations are the first to empirically suggest that using 3D facial imaging instead of 2D photography improves the performance of face-based syndrome diagnosis systems. Finally, the analysis performed for facial surgeons demonstrates that the methods developed in this thesis are applicable to medical domains other than computer-assisted diagnosis.
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Keywords
Genetic Syndrome, Computer Assisted Diagnosis, 3D Facial Surface Scan
Citation
Bannister, J. J. (2023). Computer-assisted diagnosis of genetic syndromes using 3D facial surface scans (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.