PUBLIC RELEASE DATE:

24-Jun-2014

Contact: Press Office news.office@admin.ox.ac.uk 44-186-528-0530 University of Oxford

Computer analysis of photographs could help doctors diagnose which condition a child with a rare genetic disorder has, say Oxford University researchers.

The researchers, funded in part by the Medical Research Council (MRC), 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. The study was funded by the MRC, the Wellcome Trust, the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) and the European Research Council (ERC VisRec).

While genetic disorders are each individually rare, collectively these conditions are thought to affect one person in 17. Of these, a third may have symptoms that greatly reduce quality of life. However, most people fail to receive a genetic diagnosis.

'A diagnosis of a rare genetic disorder can be a very important step. It can provide parents with some certainty and help with genetic counselling on risks for other children or how likely a condition is to be passed on,' says lead researcher Dr Christoffer Nellker of the MRC Functional Genomics Unit at the University of Oxford. 'A diagnosis can also improve estimates of how the disease might progress, or show which symptoms are caused by the genetic disorder and which are caused by other clinical issues that can be treated.'

The team of researchers at the University of Oxford included first author Quentin Ferry, a DPhil research student, and Professor Andrew Zisserman of the Department of Engineering Science, who brought expertise in computer vision and machine learning.

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Computer-aided diagnosis of rare genetic disorders from family snaps