Deep learning algorithm turns smartphone into pulse oximeter

The technique involves participants placing their finger over the camera and flash of a smartphone, which uses a deep-learning algorithm to interpret the blood oxygen levels. When the team delivered a controlled mixture of nitrogen and oxygen to six subjects to artificially bring their blood oxygen levels down, the smartphone correctly predicted whether the subject had low blood oxygen levels 80 per cent of the time.

The team, from the University of Washington and University of California San Diego, have had their results published in npj Digital Medicine.

"Other smartphone apps that do this were developed by asking people to hold their breath. But people get very uncomfortable and have to breathe after a minute or so, and that's before their blood-oxygen levels have gone down far enough to represent the full range of clinically relevant data," said co-lead author Jason Hoffman, a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering. "With our test, we're able to gather 15 minutes of data from each subject. Our data shows that smartphones could work well right in the critical threshold range."