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| [PHOTO: autism.kennedykrieger.org] |
Columbia: Advanced 3-D facial imaging may aid in early detection of autism in kids, a new study revealed.
Researchers at the University of Missouri used advanced three-dimensional imaging and statistical analysis techniques to identify facial measurements in children with autism.
Soon 3D may become an effective tool to screen young children for autism and it may provide clues to its genetic causes too, say scientists.
“We want to detect the specific facial traits of the face of a child with autism,” said Ye Duan, associate professor of computer science in the College of Engineering at MU.
“Doing so might help us define the facial structures common to children with autism and potentially enable early screening for the disorder, " Duan added.
Expanding upon previous studies using two-dimensional imaging, Duan, working with Judith Miles, professor emerita of child health-genetics in the MU Thompson Center for Autism and Neurodevelopmental Disorders, used a system of cameras to photograph and generate three-dimensional images of children’s faces.
The children selected were between 8 and 12 years old. One group of children had been diagnosed with autism by the Thompson Center; the other group consisted of typically developing children.
Researchers photographed the faces of children using three-dimensional imaging, which allowed scientists to measure distances along the curvature of the face rather than in a straight line as had been done in previous tests.
Duan then ran sophisticated statistical analyses to measure minute differences in the facial measurements of each group.
It all started from a clinical observation. Over years of treating children, I noticed that a portion of those diagnosed with autism tend to look alike with similar facial characteristics,” Miles said.
“I thought perhaps there was something more than coincidence at play. The differences were not abnormal, rather they appeared analogous to similarities observed among siblings.
“Using three-dimensional images and statistical analysis, we created a ‘fine-tuned map’ of children’s faces and compared those measurements to the various symptoms they exhibit.
“By clustering the groups based on their facial measurements and recording their autism symptoms, we wanted to determine whether subgroups based on facial structure correlate with autism symptoms and severity,” Miles added.
The group’s analyses showed three distinct subgroups of children with autism who had similar measurement patterns in their facial features. These subgroups also shared similarities in the type and severity of their autism symptoms.
The study was published in the Journal of Autism and Developmental Disorders.