Our lungs fill with oxygen when we breathe in, and our red blood cells then transfer that oxygen throughout the rest of our bodies. Our bodies require a great deal of oxygen to function, and healthy individuals have a constant oxygen saturation of at least 95%.
Conditions like asthma or COVID-19 make it more difficult for bodies to take oxygen from the lungs. As a result, oxygen saturation levels fall to 90% or lower, signalling the need for medical intervention.
Doctors use pulse oximeters, the clips you put over your fingertip or ear, to check oxygen saturation in a clinic. However, patients might benefit from routinely checking their oxygen saturation at home to keep a watch on symptoms like COVID.
Researchers from the Universities of Washington and California San Diego have demonstrated in a proof-of-concept study that smartphones can detect blood oxygen saturation levels as low as 70%. As recommended by the U.S. Food and Drug Administration, this is the minimum value that pulse oximeters should be able to measure.
Participants place a finger over a smartphone’s camera and flash; the device then employs a deep-learning algorithm to determine their blood-oxygen levels. When the team administered a regulated mixture of nitrogen and oxygen to six people in order to artificially lower their blood oxygen levels, the smartphone predicted if the patient had low blood oxygen levels 80% of the time.
The group reported their findings in npj Digital Medicine today.
“Other smartphone apps that do this were developed by asking people to hold their breath,[ says co-lead author Jason Hoffman. “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,” remarks the author.
With this new test, they were “able to gather 15 minutes of data from each subject. Our data shows that smartphones could work well right in the critical threshold range.”
Another benefit of checking blood oxygen levels on a smartphone is that practically everyone owns one.
“This way you could have multiple measurements with your own device at either no cost or low cost,” adds co-author Dr. Matthew Thompson.
“In an ideal world, this information,” according to Dr. Thompson, “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.”
The team tested this on six people whose ages ranged from 20 to 34. There were three self-identified females and three males. While the majority of participants reported as being Caucasian, one individual identified as being African American.
Each participant was required to wear a regular pulse oximeter on one finger while also placing another finger on the same hand over a smartphone camera and flash in order to collect data for training and testing the algorithm. This setup was concurrently present on both hands for each participant.
“The camera is recording a video: Every time your heart beats, fresh blood flows through the part illuminated by the flash,” adds senior author Edward Wang, who started this project as a UW doctoral student studying electrical and computer engineering and is now an assistant professor at UC San Diego’s Design Lab and the Department of Electrical and Computer Engineering.
According to Wang, who also serves as the director of the UC San Diego DigiHealth Lab, “the camera records how much that blood absorbs the light from the flash in each of the three colour channels it measures: red, green, and blue.
According to Wang, who also serves as the director of the UC San Diego DigiHealth Lab, “the camera records how much that blood absorbs the light from the flash in each of the three colour channels it measures: red, green, and blue.
“Then we can feed those intensity measurements into our deep-learning model.”
To gradually lower oxygen levels, each participant inhaled a regulated blend of oxygen and nitrogen. It took roughly 15 minutes to complete. The team collected more than 10,000 blood oxygen level values between 61% and 100% for all six subjects.
The researchers trained a deep learning algorithm to extract the blood oxygen levels using data from four of the participants. The rest of the data was used to prove that the method worked and then test it on new people to see how well it worked.
“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,” explains co-lead author Varun Viswanath, a UW alumnus who is now a doctoral student advised by Wang at UC San Diego. “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.”
By putting the system to the test on more people, the team aims to continue this research.
“One of our subjects had thick calluses on their fingers, which made it harder for our algorithm to accurately determine their blood oxygen levels,” Hoffman adds. “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.”
But, according to the researchers, this is a good starting step toward creating machine-learn-assisted biomedical devices.
“It’s so important to do a study like this,” Wang adds. “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.”
Source: 10.1038/s41746-022-00665-y
Image Credit: Getty
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