Blood oxygen levels could be measured from home with just a smartphone thanks to new research from the US.
Researchers at University of Washington and University of California San Diego demonstrated that the devices are capable of detecting blood oxygen saturation levels down to 70 per cent.
Participants placed their finger over the camera and flash of a smartphone, which used a deep-learning algorithm to decipher the blood oxygen levels.
When the team delivered a controlled mixture of nitrogen and oxygen to six subjects to artificially lower their blood oxygen levels, the smartphone correctly predicted whether the subject had low blood oxygen levels 80 per cent of the time.
Co-lead author Jason Hoffman, a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering, said:
“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.
“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.”
The research could help make blood oxygen testing more accessible than ever before.
Dr Matthew Thompson, professor of family medicine in the UW School of Medicine, said:
“This way you could have multiple measurements with your own device at either no cost or low cost.
“In an ideal world, this information could be seamlessly transmitted to a doctor’s office.
“This would be really beneficial for telemedicine appointments or for triage nurses to be able to quickly determine whether patients need to go to the emergency department or if they can continue to rest at home and make an appointment with their primary care provider later.”
Six participants aged between 20 and 34 were recruited into the study.
Three of the participants identified as female while three identified as male.
One of the participants identified as African American while the rest identified as Caucasian.
In order to gather data to train and test the algorithm, each participant wore a standard pulse oximeter on one finger and then placed another finger on the same hand over a smartphone’s camera and flash.
Each participant had this same set up on both hands at the same time.
The participants breathed in a controlled mixture of oxygen and nitrogen to slowly reduce oxygen levels, with the process taking around 15 minutes.
The team acquired a total of more than 10,000 blood oxygen level readings between 61 per cent and 100 per cent.
The researchers used data from four of the participants to train a deep learning algorithm to extract the blood oxygen levels.
The remaining data was used to validate the method and then test it to see how well it performed on new subjects.
Co-lead author Varun Viswanath, a UW alumnus who is now a doctoral student advised by Wang at UC San Diego
“Smartphone light can get scattered by all these other components in your finger, which means there’s a lot of noise in the data that we’re looking at.
“Deep learning is a really helpful technique here because it can see these really complex and nuanced features and helps you find patterns that you wouldn’t otherwise be able to see.”
The team now plans to test the algorithm on more people.
Hoffman said:
“One of our subjects had thick calluses on their fingers, which made it harder for our algorithm to accurately determine their blood oxygen levels.
“If we were to expand this study to more subjects, we would likely see more people with calluses and more people with different skin tones.
“Then we could potentially have an algorithm with enough complexity to be able to better model all these differences.”
Wang added:
“It’s so important to do a study like this.
“Traditional medical devices go through rigorous testing.
“But computer science research is still just starting to dig its teeth into using machine learning for biomedical device development and we’re all still learning.
“By forcing ourselves to be rigorous, we’re forcing ourselves to learn how to do things right.”
Image: Dennis Wise/University of Washington
