New AI Can Spot Tell-Tale Signs Of A Genetic Disorder By Scanning People’s Faces
An artificially intelligent computer program has been used to identify rare genetic diseases by studying photos of faces.
In the experiment, the AI system out-performed human experts attempting the same task.
The face analysis program, known as DeepGestalt, could in future assist the diagnosis of rare genetic syndromes, say researchers.
At the same time they warn that safeguards are needed to prevent abuse of the technology.
Easily accessible portrait photos could, for instance, enable potential employers to discriminate against individuals with ‘at risk’ facial features.
Study co-author Dr Karen Gripp, from the US company FDNA which developed the program, said: “This is a long-awaited breakthrough in medical genetics that has finally come to fruition.
“With this study, we’ve shown that adding an automated facial analysis framework, such as DeepGestalt, to the clinical workflow can help achieve earlier diagnosis and treatment, and promise an improved quality of life.”
The team trained the “deep learning” software using more than 17,000 facial images of patients with more than 200 different genetic disorders.
In subsequent tests DeepGestalt successfully included the correct syndrome in its top 10 list of suggestions 91 percent of the time.
The system also out-performed clinical experts in three separate trials. Many genetic disorders are associated with distinct facial features.
Some are easily recognizable while others are harder to spot.
People with Williams syndrome, for instance, have short, upturned noses and mouths, a small jaw and a large forehead.
Well-known features associated with Down’s syndrome include almond-shaped eyes, a round, flat face, and a small nose and mouth.
Yaron Gurovich, chief technology office at FDNA and first author of the research published in the journal Nature Medicine, said: “The increased ability to describe phenotype in a standardized way opens the door to future research and applications, and the identification of new genetic syndromes.”
Writing in the journal, the researchers drew attention to the potential risk of abuse of the technology.
They warned: “Unlike genomic data, facial images are easily accessible.
“Payers or employers could potentially analyse facial images and discriminate based on the probability of individuals having pre-existing conditions or developing medical complications.”
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