Digital facial dysmorphology for genetic screening: Hierarchical constrained local model using ICA.
Down syndrome, the most common single cause of human birth defects, produces alterations in physical growth and mental retardation. If missed before birth, the early detection of Down syndrome is crucial for the management of patients and disease. However, the diagnostic accuracy for pediatricians prior to cytogenetic results is moderate and the access to specialists is limited in many social and low-economic areas. In this study, we propose a simple, non-invasive and automated framework for Down syndrome detection based on disease-specific facial patterns. Geometric and local texture features are extracted based on automatically detected anatomical landmarks to describe facial morphology and structure. To accurately locate the anatomical facial landmarks, a hierarchical constrained local model using independent component analysis (ICA) is proposed. We also introduce a data-driven ordering method for selecting dominant independent components in ICA. The hierarchical structure of the model increases the accuracy of landmark detection by fitting separate models to different groups. Then the most representative features are selected and we also demonstrate that they match clinical observations. Finally, a variety of classifiers are evaluated to discriminate between Down syndrome and healthy populations. The best performance achieved 0.967 accuracy and 0.956 F1 score using combined features and linear discriminant analysis. The method was also validated on a dataset with mixed genetic syndromes and high performance (0.970 accuracy and 0.930 F1 score) was also obtained. The promising results indicate that our method could assist in Down syndrome screening effectively in a simple, non-invasive way, and extensible to detection of other genetic syndromes.
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC, United States. Electronic address: kate.qzhao@gmail.com.
Qian
Q
Zhao
Computer Science Department, San Francisco State University, San Francisco, CA, United States.
Kazunori
K
Okada
Division of Genetics and Metabolism, Children's National Medical Center, Washington, DC, United States.
Kenneth
K
Rosenbaum
Division of Genetics and Metabolism, Children's National Medical Center, Washington, DC, United States.
Lindsay
L
Kehoe
Division of Genetics and Metabolism, Children's National Medical Center, Washington, DC, United States.
Dina J
DJ
Zand
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC, United States; Department of Radiology, Children's National Medical Center, Washington, DC, United States.
Raymond
R
Sze
Division of Genetics and Metabolism, Children's National Medical Center, Washington, DC, United States.
Marshall
M
Summar
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC, United States; School of Medicine and Health Sciences, George Washington University, Washington, DC, United States.
Marius George
MG
Linguraru
D059925:Anatomic Landmarks / Q000473:pathology; D002675:Child, Preschool; D003198:Computer Simulation; D004314:Down Syndrome / Q000235:genetics* / Q000473:pathology*; D005145:Face / Q000002:abnormalities* / Q000473:pathology*; D005260:Female; D005820:Genetic Testing / Q000379:methods*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008953:Models, Anatomic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
Computer-aided diagnosis; Down syndrome; Hierarchical constrained local model; Independent component analysis; Machine learning