Other novel FASD diagnostic techniques include ways to identify potential at-risk individuals based upon subtle, subclinical facial features. In particular, researchers have developed a computerized method for detecting facial features using three-dimensional facial imaging and computer-based dense-surface modeling (see figure 3). Hammond and colleagues (Suttie et al. 2013) compared this approach with a standard dysmorphology exam for diagnosing FAS and found a high degree of agreement. The researchers used sophisticated mathematical techniques to characterize the facial features of heavily exposed individuals who did not have facial features that would have led to a diagnosis of FAS using traditional measures. They categorized participants as having facial features that were either “more similar to those with FAS” or “more similar to unexposed controls.” Importantly, the heavily exposed children with FAS-like faces performed at a level similar to the FAS group on neurobehavioral tests, whereas those with more control-like faces exhibited behavioral profiles similar to control subjects. These data were collected on a homogenous ethnic group in South Africa and therefore need to be replicated in other populations. Still, they provide preliminary evidence that this approach may constitute a means to identify at-risk individuals based upon subtle, sub-clinical facial features.
