Smartphones could be used to measure amount of oxygen in people’s blood

By Mark Waghorn via SWNS

A smartphone could measure the amount of oxygen in people's blood, according to new research.

It involves placing a finger over the camera and flash. Artificial intelligence (AI) deciphers levels from flow patterns in the resulting video.

The technique could spot early signs of dangerous falls in COVID-19 patients - and predict asthma attacks before they occur.

Our bodies need 95 percent' oxygen saturation'. Respiratory diseases can cause them to drop below 90 percent - when an inhalation tube or mask is required.

The device was accurate 80 percent of the time when a chemical cocktail was used to bring quantities down in young volunteers.

Study co-lead author Jason Hoffman, a doctoral student, 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."

When we breathe in our lungs fills with oxygen. It's transported to other organs by red blood cells - providing an indication of fitness and heart health.

Coronaviruses and allergies impair absorption. In a clinic monitors called pulse oximeters are used - clips placed over a fingertip or ear.

A smartphone would be much more effective - as "almost everyone has one," say the team at the University of Washington in Seattle.

Co-author Professor Matthew Thompson explained: "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."

People could keep an eye on Covid symptoms, for example, "multiple times a day.," said the researchers.

The study found smartphones are capable of detecting oxygen levels down to 70 percent - the lowest value pulse oximeters should be able to measure.

A computer neural network, or deep learning algorithm, was trained on six participants aged 20 to 34.

They used a smartphone while wearing a standard pulse oximeter on one finger at the same time.

Senior author Dr. Edward Wang, now at California University in San Diego, 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 color channels it measures - red, green and blue. Then we can feed those intensity measurements into our deep-learning model."

Each person breathed in a controlled mixture, which included nitrogen, to slowly reduce oxygen.

The process took about 15 minutes. More than 10,000 readings were acquired - between 61 and 100 percent. The algorithm now needs testing 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."

The study, published in the journal npj Digital Medicine, is a good first step toward developing biomedical devices that are aided by machine learning.

Dr. 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."