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Smartphone Camera And Flash Could Help Measure Blood Oxygen Levels

A standard modern smartphone is capable of detecting blood oxygen saturation levels down to 70 per cent, according to a study by researchers from the University of Washington and the University of California.

The research team has demonstrated a technique that involves participants placing their finger over the camera and flash of a phone, whereafter a deep-learning algorithm deciphers the blood oxygen levels from the blood flow patterns captured in the resulting video.

When we breathe in, our lungs fill with oxygen, which is distributed to our red blood cells for transportation throughout our bodies. Our bodies need a lot of oxygen to function, and healthy people have at least 95 per cent oxygen saturation all the time.

Conditions like asthma or Covid-19 make it harder for bodies to absorb oxygen from the lungs. This leads to oxygen saturation percentages that drop to 90 per cent or below – an indication that medical attention may be needed.

In a clinic, doctors monitor oxygen saturation using pulse oximeters: clips placed over a patient’s fingertip or ear. Being able to monitor oxygen saturation at home multiple times a day could, for example, help patients keep an eye on Covid symptoms.

In their proof-of-principle study, the University of Washington and University of California San Diego researchers showed that smartphones are capable of detecting blood oxygen saturation levels down to 70 per cent. This is the lowest value that pulse oximeters should be able to measure, as recommended by the US Food and Drug Administration.

The team recruited six participants ranging in age from 20 to 34. Three identified as female; three identified as male. One participant identified as being African American, while the rest identified as being Caucasian.

To gather data to train and test the algorithm, the researchers had each participant wear a standard pulse oximeter on one finger and then place another finger on the same hand over a smartphone’s camera and flash. Each participant had this same set-up on both hands simultaneously.

Using the smartphone camera technique described above, the team delivered a controlled mixture of nitrogen and oxygen to six subjects to artificially bring their blood oxygen levels down. The smartphone was able to correctly predict whether the subject had low blood oxygen levels 80 per cent of the time.

“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.”

Another benefit of measuring blood oxygen levels on a smartphone is that almost everyone already owns such a device.

Edward Wang, senior author on the research paper, and now an assistant professor at UC San Diego’s Design Lab and the Department of Electrical and Computer Engineering, said: “The camera is recording a video: Every time your heart beats, fresh blood flows through the part illuminated by the flash.

“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.”

Each participant in the tests breathed in a controlled mixture of oxygen and nitrogen to slowly reduce their oxygen levels. The process took about 15 minutes. For all six participants, the team acquired 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 pull out the blood oxygen levels. The remainder of the data was used to validate the method and then test it to see how well it performed on new subjects.

“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,” said co-lead author Varun Viswanath, a UW alumnus and 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.”

The team hopes to continue this research by testing the algorithm on more people.

“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 said. “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.”

The researchers said that the results so far represent a good first step toward developing biomedical devices that are aided by machine learning.

“It’s so important to do a study like this,” Wang said. “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: E&T

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