Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Filters applied. Clear all
. 2017 Dec;140(6):e20162028.
doi: 10.1542/peds.2016-2028.

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

Affiliations
Free PMC article

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

Matthew Valentine et al. Pediatrics. .
Free PMC article

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 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.

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.

Conflict of interest statement

POTENTIAL CONFLICT OF INTEREST: Dr Wolf is a cofounder of FDNA Inc; the other authors have indicated they have no potential conflicts of interest to disclose.

Figures

FIGURE 1
FIGURE 1
Demonstration of FDNA technology.
FIGURE 2
FIGURE 2
ROC curves obtained for the 4 recognition experiments. Each plot compares the performance of a manual score based on DSS (solid) to a computer-aided one (hatched). A, FAS. B, PFAS. C, ARND. D, Any FASD.
FIGURE 3
FIGURE 3
A, The FAS heatmap overlaid on the input image. B, The same heatmap overlaid on a typical FAS face. C, Heatmap index. A facial region most supportive of the FAS classification is marked in red, while regions that are less supportive are marked with cooler colors.
FIGURE 4
FIGURE 4
Heatmap comparison of FASD showing all regions of the face provide cues supporting ARND classification; in FAS and PFAS, only few regions that correspond to the cardinal facial features support the diagnosis of FASD. The images show regions supporting classification of A, ARND; B, FAS; and C, PFAS. D, Correlation scale.

Similar articles

See all similar articles

Cited by 7 articles

See all "Cited by" articles
Feedback