Computer-aided recognition of facial attributes for fetal alcohol spectrum disorders

Matthew Valentine, Dustin C.J. Bihm, Lior Wolf, H. Eugene Hoyme, Philip A. May, David Buckley, Wendy Kalberg, Omar A. Abdul-Rahman

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

OBJECTIVES: To compare the detection of facial attributes by computer-based facial recognition software of 2-D images against standard, manual examination in fetal alcohol spectrum disorders (FASD). METHODS: Participants were gathered from the Fetal Alcohol Syndrome Epidemiology Research database. Standard frontal and oblique photographs of children were obtained during a manual, in-person dysmorphology assessment. Images were submitted for facial analysis conducted by the facial dysmorphology novel analysis technology (an automated system), which assesses ratios of measurements between various facial landmarks to determine the presence of dysmorphic features. Manual blinded dysmorphology assessments were compared with those obtained via the computer-aided system. RESULTS: Areas under the curve values for individual receiver-operating characteristic curves revealed the computer-aided system (0.88 ± 0.02) to be comparable to the manual method (0.86 ± 0.03) in detecting patients with FASD. Interestingly, cases of alcohol-related neurodevelopmental disorder (ARND) were identified more efficiently by the computeraided system (0.84 ± 0.07) in comparison to the manual method (0.74 ± 0.04). A facial gestalt analysis of patients with ARND also identified more generalized facial findings compared to the cardinal facial features seen in more severe forms of FASD. CONCLUSIONS: We found there was an increased diagnostic accuracy for ARND via our computer-aided method. As this category has been historically difficult to diagnose, we believe our experiment demonstrates that facial dysmorphology novel analysis technology can potentially improve ARND diagnosis by introducing a standardized metric for recognizing FASD-associated facial anomalies. Earlier recognition of these patients will lead to earlier intervention with improved patient outcomes.

Original languageEnglish (US)
Article numbere20162028
JournalPediatrics
Volume140
Issue number6
DOIs
StatePublished - Dec 2017

ASJC Scopus subject areas

  • Pediatrics, Perinatology, and Child Health

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    Valentine, M., Bihm, D. C. J., Wolf, L., Hoyme, H. E., May, P. A., Buckley, D., Kalberg, W., & Abdul-Rahman, O. A. (2017). Computer-aided recognition of facial attributes for fetal alcohol spectrum disorders. Pediatrics, 140(6), [e20162028]. https://doi.org/10.1542/peds.2016-2028