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Pediatrics. 2017 Dec;140(6). pii: e20162028. doi: 10.1542/peds.2016-2028.

Computer-Aided Recognition of Facial Attributes for Fetal Alcohol Spectrum Disorders.

Author information

Department of Pediatrics, University of Mississippi Medical Center, Jackson, Mississippi.
FDNA Inc, Boston, Massachusetts.
Sanford Research and School of Medicine, University of South Dakota Sanford, Sioux Falls, South Dakota.
Center for Applied Genetics and Genomic Medicine and Department of Pediatrics, College of Medicine, University of Arizona, Tucson, Arizona.
Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Center on Alcoholism, Substance Abuse, and Addictions, The University of New Mexico, Albuquerque, New Mexico; and.
Department of Genetic Medicine, Munroe-Meyer Institute for Genetics and Rehabilitation, University of Nebraska Medical Center, Omaha, Nebraska



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).


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.


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 computer-aided 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.


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.

[Indexed for MEDLINE]
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