Blood oxygen measurement with smartphone camera
Researchers at the University of Washington have stated in their recent study that smartphone cameras can track your blood oxygen levels.
According to Soraya, smartphone cameras will soon be able to help measure your blood oxygen level.
Washington University researchers have stated in their recent study, this technology includes placing a finger on the camera and flash; Artificial intelligence (AI) then decodes the blood oxygen levels from the patterns recorded in the video. This method can identify the early signs of a dangerous drop in oxygen levels among patients with Covid-19 and predict asthma attacks before they occur.
Our body needs 95% oxygen saturation. Respiratory diseases can reduce this amount to less than 90%, and that’s when a person needs breathing tubes or oxygen masks.
The results of this study show that when researchers used a chemical cocktail to reduce the amounts in young volunteers, the performance of this device was accurate 80% of the time.
“Other smartphone apps that do this are made by asking people to hold their breath,” said Paul G. Allen, a student at the University of Washington’s School of Computer Science and Engineering. But people aren’t very comfortable with the program, and are forced to take a breath after a minute or so, and that’s before their blood oxygen levels drop enough to show the full range of clinically relevant data. With our experiment, we can collect 15 minutes of data from each subject. Our data shows that smartphones can work well in the critical threshold range. Information can also be seamlessly transmitted to the doctor’s office.
When we breathe, our lungs are filled with oxygen. It is transported by red blood cells to other organs, which is a sign of fitness and heart health. Viruses and allergies interfere with oxygen absorption. The results of this study showed that smartphones can detect oxygen levels up to 70%, which is the lowest value for pulse oximeters. The researchers trained a computer neural network, or deep learning algorithm, to measure oxygen levels in six participants between the ages of 20 and 34.
The findings of this study, published in the journal NPJ Digital Medicine, are the first step towards the development of biomedical devices that work with machine learning to produce better results.