By Alice Philipson. Newly developed facial-recognition software allows a computer to scour family snaps for facial features characteristic of conditions such as Down's syndrome. In future, it could be used to identify people born with such disorders, allowing them to be given early treatment. The computer software, developed at the Universities of Edinburgh and Oxford, employs a form of "artificial intelligence" to learn what aspects of a person's face are linked to particular conditions.
This AI Identifies Genetic Disorders by Looking at Face Shape
Face map: Boys with autism have broader faces and mouths, flatter noses and narrower cheeks than controls do. Boys with autism have a distinct facial structure that differs from that of typically developing controls, according to a study published 14 October in Molecular Autism 1. Specifically, boys with autism have broader faces and mouths, flatter noses, narrower cheeks and a shorter philtrum — the cleft between the lips and nose — compared with controls, according to the three-dimensional facial imaging system used in the study. These distinctive features suggest that certain embryonic processes that give rise to facial features are perturbed during development, the researchers say. The participants in the study were all 8 to 12 years old, an age range during which the face is relatively mature, but not yet affected by the hormonal changes of puberty. They then measured the distance between several of these coordinates.
Detecting Visually Observable Disease Symptoms from Faces
Computer analysis of photographs could help doctors diagnose which condition a child with a rare genetic disorder has, say Oxford University researchers. Oxford University researchers have come up with a computer programme that recognises facial features in photographs; looks for similarities with facial structures for various conditions, such as Down's syndrome, Angelman syndrome, or Progeria; and returns possible matches ranked by likelihood. Using the latest in computer vision and machine learning, the algorithm increasingly learns what facial features to pay attention to and what to ignore from a growing bank of photographs of people diagnosed with different syndromes. The researchers report their findings in the journal eLife.
Researchers at the National Institutes of Health have successfully used facial recognition software to diagnose a rare, genetic disease in Africans, Asians and Latin Americans. Affecting children, DiGeorge syndrome is a genetic disorder that results in multiple defects throughout the body, including cleft palate, heart defects, a characteristic facial appearance and learning problems. Diagnosing the disease is difficult, especially in non-European populations, because the appearance of those with the disease varies widely and the only available diagnostic tool until now featured photos of patients with northern European ancestry. Based on individual facial features, researchers made correct diagnoses for all ethnic groups