With 30 years' experience in print and digital media, Helen has written articles for clients ranging from The Sydney Morning Herald, The Australian Financial Review and GQ Australia, to CommBank, Swissotels and Bunnings. Her father was a civil engineer and she remains fascinated by the use of scientific principles to design and build a better world.

And now, an algorithm can predict not only whether they carry a genetic mutation, but which genes were mutated. The study, published Monday in Nature Medicine, is the latest from a Boston-based company called FDNA, one of a few organizations creating software that can help physicians diagnose genetic syndromes based just on a face -- and may serve an important validation of the company's technology, said Yaron Gurovich, the company's chief technology officer. "We went for this high-impact journal to prove beyond any doubt that this technology is good, it performs as we say, we can stand behind it, and now it opens a lot of doors to publish more," he said. The study itself is a collection of experiments testing how the results of algorithms -- FDNA refers to them as DeepGestalt -- stack up against clinicians' diagnoses. In one of the experiments, DeepGestalt's performance was better than random chance when picking which of five genetic mutations might be causing a condition called Noonan syndrome.