Getting Closer to the Day When Smart Watches Become Star Trek Medical Tricorders. (A Device that can tell all your Medical Data Unintrusively.)

Apple Watch wristband sensor claims to detect potassium in your blood - without needles

The AliveCor KardiaBand, a sensor compatible with the Apple Watch, can detect dangerous levels of potassium in blood with 94 percent accuracy. Though the US Food and Drug Administration has not yet approved KardiaBand for this purpose, it's an interesting step forward considering that, right now, the condition is usually caught using invasive blood tests that use needles.

 From article, (The AliveCor KardiaBand, a sensor compatible with the Apple Watch, can detect dangerous levels of potassium in blood with 94 percent accuracy. Though the US Food and Drug Administration has not yet approved KardiaBand for this purpose, it’s an interesting step forward considering that, right now, the condition is usually caught using invasive blood tests that use needles.

The KardiaBand by AliveCor is a sensor that snaps into a slot on the watchband. The user touches the sensor, which then takes a reading of the electrical activity of the heart, called an electrocardiogram (EKG). This reading can reveal abnormal heart rhythm and atrial fibrillation (AFib), and the sensor sends the information to an app. Yesterday, at the American College of Cardiology conference in Florida, AliveCor CEO Vic Gundotra presented research done with the Mayo Clinic showing that the same technology can detect too-high levels of potassium in the blood, called hyperkalemia.

Hyperkalemia can be caused by, among other things, diabetes, dehydration, and chronic kidney disease. It can lead to kidney and heart failure and in general doesn’t cause obvious symptoms — meaning you could have the condition and not know it.

Too much potassium interferes with the electrical activity of cells, including heart cells. This means that it’s dangerous for the heart — but it also means that high potassium levels change the electrical reading of the heart, which means that a certain EKG pattern can reveal the presence of too much potassium, according to Gundotra. AliveCor worked with the Mayo Clinic to develop a new algorithm for the KardiaBand that can analyze EKG data and detect whether the user has hyperkalemia. The dataset included 2 million EKGs linked to 4 million potassium values, which were collected over 23 years.

To train AI with these data points, the team took the dataset and divided it into parts. They used some of the data to train the network. Basically, they told it which EKG reading patterns showed hyperkalemia, and let the AI learn for itself how to spot the pattern. Once the training was complete, the team tested the AI on a different part of the data to see if, given just the EKG, they could tell if it revealed hyperkalemia. It was about 90 to 94 percent accurate.)

For More Info