A facial recognition scan could become part of a standard medical checkup in the not-too-distant future. Researchers have shown how algorithms can help identify facial characteristics linked to genetic disorders, potentially speeding up clinical diagnoses.
In a study published this month in the journal Nature Medicine, US company FDNA published new tests of their software, DeepGestalt. Just like regular facial recognition software, the company trained their algorithms by analyzing a dataset of faces. FDNA collected more than 17,000 images covering 200 different syndromes using a smartphone app it developed named Face2Gene.
In two first tests, DeepGestalt was used to look for specific disorders: Cornelia de Lange syndrome and Angelman syndrome. Both of these are complex conditions that affect intellectual development and mobility. They also have distinct facial traits, like arched eyebrows that meet in the middle for Cornelia de Lange syndrome, and unusually fair skin and hair for Angelman syndrome.
When tasked with distinguishing between pictures of patients with one syndrome or another, random syndrome, DeepGestalt was more than 90 percent accurate, beating expert clinicians, who were around 70 percent accurate on similar tests.
When tested on 502 images showing individuals with 92 different syndromes, DeepGestalt identified the target condition in its guess of 10 possible diagnoses more than 90 percent of the time.
