Cardiogram DeepHeart detects diabetes through smartwatches

A new study from Cardiogram suggests that ordinary wearables like Fitbit and Apple watches can detect diabetes and other medical conditions when integrated with Cardiogram’s DeepHeart app.

For the study, Cardiogram used more than 200 million sensor measurements from 14,011 participants using an Apple Watch or Android Wear device and the Cardiogram app, aggregating data that included heart rate, step count, and other activity. MacRumors

By Laura Lovett, MobiHealthNews February 07, 2018

“Twenty-four percent of people with diabetes, and 88.4 percent with pre-diabetes, don’t realize they have it,” Brandon Ballinger, CEO of Cardiogram, told MobiHealthNews in an email. “We don’t want to turn people into patients, but by catching diabetes early, we can guide people to convenient treatments they can perform in their everyday lives, like diabetes prevention programs. Ultimately, this means healthier patients and cheaper healthcare for all of us — for example, Medicare recently certified $2,650 in cost savings for every person with pre-diabetes that enrolls in a diabetes prevention program.”

The study, which was funded by Cardiogram and conducted in partnership with the University of California San Francisco, found that the DeepHeart app was 85 percent accurate in distinguishing between people with and without diabetes. The app was also able to detect high blood pressure with 80 percent accuracy, and sleep apnea with 83 percent accuracy.

Cardiogram’s DeepHeart uses deep neural network technology and machine learning to analyze large sets of data.

The new study adds to evidence that the right algorithms might transform the Apple Watch from personal trainer to personal physician. Cardiogram

The study, which was presented at this week’s AAAI Conference on Artificial Intelligence, had 14,011 participants from all over the world, and collected 200 million unlabeled sensor measurements. Participants completed a medical history, which included previous diagnoses and medications. They were also given a mobile app, which integrated with HealthKit and continuously stored and processed the participants’ heart rate steps and daily activity, according to the study.

Researchers then compared two semi-supervised training methods and found that both significantly better than baseline analyses. The authors claim that these methods also outperformed hand-engineered biomarkers for detecting health conditions that have been reported in past medical literature.

“We believe our work suggests a new approach to patient risk stratification based on cardiovascular risk scores derived from popular wearables such as Fitbit, Apple Watch, or Android Wear,” the authors of the study wrote.

In November, another study released by Cardiogram and the UC San Fransisco Health eHeart Study found that DeepHeart could accurately detect hypertension and sleep apnea. However, diabetes was not measured in that investigation.

This is just one of a number of studies that is set to validate the product and, according to the company, pave the way for use as an intervention.

“Phase 1 of Cardiogram was validation: proving that wearables, when combined with artificial intelligence, could accurately detect health conditions like diabetes, hypertension, sleep apnea, and atrial fibrillation,” Ballinger wrote. “To that end, over the last year or so, we’ve published five validation studies in academic medical or artificial intelligence conferences.

Phase 2 of Cardiogram is intervention: actually helping our users become healthier in real-world scenarios. Over the next few months, you’ll see us launch new features that guide you through the process of screening, confirmation, and referral to clinically-appropriate treatments for diabetes, pre-diabetes, and more.”

But the product isn’t just designed to help diabetes. Ballinger explains that it is set up with all heart related conditions in mind.

“Since your heart is connected to your pancreas, blood vessels, brain, stomach, intestines, and more through the autonomic nervous system, we think heart rate is not only a reflection of your heart health, but also a powerful vantage point into the rest of your body,” said Ballinger.

Source MobiHealthNews

DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction, Brandon Ballinger, Johnson Hsieh, Avesh Singh, Nimit Sohoni, Jack Wang, Geoffrey H Tison, Gregory M Marcus, Jose M Sanchez, Carol Maguire, Jeffrey E Olgin, Mark J Pletcher. 32nd  AAAI Conference on Artificial Intelligence, Feb 2018. ArXiv e-prints. 2018arXiv180202511B

Abstract 21042: Cardiovascular Risk Stratification Using Off-the-Shelf Wearables and a Multi-Task Deep Learning Algorithm, Geoffrey H Tison, Avesh C Singh, Daniel A Ohashi, Johnson T Hsieh, Brandon M Ballinger, Jeffrey E Olgin, Gregory M Marcus, Mark J Pletcher. Circulation. 2017;136:A21042, originally published November 11, 2017

Abstract 21029: Achieving High Retention in Mobile Health Research Using Design Principles Adopted From Widely Popular Consumer Mobile Apps, Geoffrey H Tison, Kaiyu Hsu, Johnson T Hsieh, Brandon M Ballinger, Mark J Pletcher, Gregory M Marcus, Jeffrey E Olgin. Circulation. 2017;136:A21029, originally published November 11, 2017

C-AB12-01 / C-AB12-01 – Detecting Atrial Fibrillation using a Smart Watch – the mRhythm study, Jose M Sanchez MD, Brandon Ballinger BS, Jeffrey E Olgin MD, FHRS, Mark J Pletcher MD MPH, Eric Vittinghoff PhD, Emily Lee BA, Shannon Fan BA, Nimit Sohoni BS, Carlos Mikell BS, Johnson Hsieh MS, Rachel A Gladstone BA, and Gregory M Marcus MD FHRS. (University of California San Francisco – Div. of Cardiology Cardiac EP, San Francisco, CA, (Cardiogram Inc, San Francisco, CA, University of California San Francisco – Department of Epidemiology and Biostatistics, San Francisco, CA, University of California San Francisco – School of Medicine, San Francisco, CA, Queen’s University – School of Medicine, Kingston, ON, Canada) Heart Rhythm Society, HRS 2017, May 10-13, 2017 Chicago

Also see
Ordinary wearables can flag signs of diabetes, according to new Cardiogram study in Upbeat
Skin-Like Biosensor Offers Needle-Free Blood Sugar Monitoring in IEEE Spectrum
New Study Suggests Apple Watch Heart Rate Sensor Can Detect Early Signs of Diabetes in MacRumors
Apple Watch: The ‘Check Engine Light’ For Our Bodies in American Council on Science and Health ACSH
Apple Watch paired with deep neural network detects atrial fibrillation with 97 percent accuracy in MobiHealthNews
AI can help apple watch predict high blood pressure, sleep apnea in Wired
How Cardiogram is unlocking secret powers of the humble heart rate sensor in Wearable
DeepHeart AI IDs sleep apnea, hypertension via Apple Watch in Engadget
AI can help Apple Watch predict high blood pressure, sleep apnea in Wired
Wearables could catch heart problems that elude your doctor in Medical Xpress

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