wearable devices
Wearable Sensor Promises More Efficient Early Cancer Drug Development
Posted on by Lawrence Tabak, D.D.S., Ph.D.
Wearable electronic sensors hold tremendous promise for improving human health and wellness. That promise already runs the gamut from real-time monitoring of blood pressure and abnormal heart rhythms to measuring alcohol consumption and even administering vaccines.
Now a new study published in the journal Science Advances [1] demonstrates the promise of wearables also extends to the laboratory. A team of engineers has developed a flexible, adhesive strip that, at first glance, looks like a Band-Aid. But this “bandage” actually contains an ultra-sensitive, battery-operated sensor that’s activated when placed on the skin of mouse models used to study possible new cancer drugs.
This sensor is so sensitive that it can detect, in real time, changes in the size of a tumor down to one-hundredth of a millimeter. That’s about the thickness of the plastic cling wrap you likely have in your kitchen! The device beams those measures to a smartphone app, capturing changes in tumor growth minute by minute over time.
The goal is to determine much sooner—and with greater automation and precision—which potential drug candidates undergoing early testing in the lab best inhibit tumor growth and, consequently, should be studied further. In their studies in mouse models of cancer, researchers found the new sensor could detect differences between tumors treated with an active drug and those treated with a placebo within five hours. Those quick results also were validated using more traditional methods to confirm their accuracy.
The device is the work of a team led by Alex Abramson, a former post-doc with Zhenan Bao, Stanford University’s School of Engineering, Palo Alto, CA. Abramson has since launched his own lab at the Georgia Institute of Technology, Atlanta.
The Stanford team began looking for a technological solution after realizing the early testing of potential cancer drugs typically requires researchers to make tricky measurements using pincer-like calipers by hand. Not only is the process tedious and slow, it’s less than an ideal way to capture changes in soft tissues with the desired precision. The imprecision can also lead to false leads that won’t pan out further along in the drug development pipeline, at great time and expense to their developers.
To refine the process, the NIH-supported team turned to wearable technology and recent advances in flexible electronic materials. They developed a device dubbed FAST (short for Flexible Autonomous Sensor measuring Tumors). Its sensor, embedded in a skin patch, is composed of a flexible and stretchable, skin-like polymer with embedded gold circuitry.
Here’s how FAST works: Coated on top of the polymer skin patch is a layer of gold. When stretched, it forms small cracks that change the material’s electrical conductivity. As the material stretches, even slightly, the number of cracks increases, causing the electronic resistance in the sensor to increase as well. As the material contracts, any cracks come back together, and conductivity improves.
By picking up on those changes in conductivity, the device measures precisely the strain on the polymer membrane—an indication of whether the tumor underneath is stable, growing, or shrinking—and transmits that data to a smartphone. Based on that information, potential therapies that are linked to rapid tumor shrinkage can be fast-tracked for further study while those that allow a tumor to continue growing can be cast aside.
The researchers are continuing to test their sensor in more cancer models and with more therapies to extend these initial findings. Already, they have identified at least three significant advantages of their device in early cancer drug testing:
• FAST is non-invasive and captures precise measurements on its own.
• It can provide continuous monitoring, for weeks, months, or over the course of study.
• The flexible sensor fully surrounds the tumor and can therefore detect 3D changes in shape that would be hard to pick up otherwise in real-time with existing technologies.
By now, you are probably asking yourself: Could FAST also be applied as a wearable for cancer patients to monitor in real-time whether an approved chemotherapy regimen is working? It is too early to say. So far, FAST has not been tested in people. But, as highlighted in this paper, FAST is off to, well, a fast start and points to the vast potential of wearables in human health, wellness, and also in the lab.
Reference:
[1] A flexible electronic strain sensor for the real-time monitoring of tumor regression. Abramson A, Chan CT, Khan Y, Mermin-Bunnell A, Matsuhisa N, Fong R, Shad R, Hiesinger W, Mallick P, Gambhir SS, Bao Z. Sci Adv. 2022 Sep 16;8(37):eabn6550.
Links:
Stanford Wearable Electronics Initiative (Stanford University, Palo Alto, CA)
Bao Group (Stanford University)
Abramson Lab (Georgia Institute of Technology, Atlanta)
NIH Support: National Institute of Biomedical Imaging and Bioengineering
Using Artificial Intelligence to Catch Irregular Heartbeats
Posted on by Dr. Francis Collins
Thanks to advances in wearable health technologies, it’s now possible for people to monitor their heart rhythms at home for days, weeks, or even months via wireless electrocardiogram (EKG) patches. In fact, my Apple Watch makes it possible to record a real-time EKG whenever I want. (I’m glad to say I am in normal sinus rhythm.)
For true medical benefit, however, the challenge lies in analyzing the vast amounts of data—often hundreds of hours worth per person—to distinguish reliably between harmless rhythm irregularities and potentially life-threatening problems. Now, NIH-funded researchers have found that artificial intelligence (AI) can help.
A powerful computer “studied” more than 90,000 EKG recordings, from which it “learned” to recognize patterns, form rules, and apply them accurately to future EKG readings. The computer became so “smart” that it could classify 10 different types of irregular heart rhythms, including atrial fibrillation (AFib). In fact, after just seven months of training, the computer-devised algorithm was as good—and in some cases even better than—cardiology experts at making the correct diagnostic call.
EKG tests measure electrical impulses in the heart, which signal the heart muscle to contract and pump blood to the rest of the body. The precise, wave-like features of the electrical impulses allow doctors to determine whether a person’s heart is beating normally.
For example, in people with AFib, the heart’s upper chambers (the atria) contract rapidly and unpredictably, causing the ventricles (the main heart muscle) to contract irregularly rather than in a steady rhythm. This is an important arrhythmia to detect, even if it may only be present occasionally over many days of monitoring. That’s not always easy to do with current methods.
Here’s where the team, led by computer scientists Awni Hannun and Andrew Ng, Stanford University, Palo Alto, CA, saw an AI opportunity. As published in Nature Medicine, the Stanford team started by assembling a large EKG dataset from more than 53,000 people [1]. The data included various forms of arrhythmia and normal heart rhythms from people who had worn the FDA-approved Zio patch for about two weeks.
The Zio patch is a 2-by-5-inch adhesive patch, worn much like a bandage, on the upper left side of the chest. It’s water resistant and can be kept on around the clock while a person sleeps, exercises, or takes a shower. The wireless patch continuously monitors heart rhythms, storing EKG data for later analysis.
The Stanford researchers looked to machine learning to process all the EKG data. In machine learning, computers rely on large datasets of examples in order to learn how to perform a given task. The accuracy improves as the machine “sees” more data.
But the team’s real interest was in utilizing a special class of machine learning called deep neural networks, or deep learning. Deep learning is inspired by how our own brain’s neural networks process information, learning to focus on some details but not others.
In deep learning, computers look for patterns in data. As they begin to “see” complex relationships, some connections in the network are strengthened while others are weakened. The network is typically composed of multiple information-processing layers, which operate on the data and compute increasingly complex and abstract representations.
Those data reach the final output layer, which acts as a classifier, assigning each bit of data to a particular category or, in the case of the EKG readings, a diagnosis. In this way, computers can learn to analyze and sort highly complex data using both more obvious and hidden features.
Ultimately, the computer in the new study could differentiate between EKG readings representing 10 different arrhythmias as well as a normal heart rhythm. It could also tell the difference between irregular heart rhythms and background “noise” caused by interference of one kind or another, such as a jostled or disconnected Zio patch.
For validation, the computer attempted to assign a diagnosis to the EKG readings of 328 additional patients. Independently, several expert cardiologists also read those EKGs and reached a consensus diagnosis for each patient. In almost all cases, the computer’s diagnosis agreed with the consensus of the cardiologists. The computer also made its calls much faster.
Next, the researchers compared the computer’s diagnoses to those of six individual cardiologists who weren’t part of the original consensus committee. And, the results show that the computer actually outperformed these experienced cardiologists!
The findings suggest that artificial intelligence can be used to improve the accuracy and efficiency of EKG readings. In fact, Hannun reports that iRhythm Technologies, maker of the Zio patch, has already incorporated the algorithm into the interpretation now being used to analyze data from real patients.
As impressive as this is, we are surely just at the beginning of AI applications to health and health care. In recognition of the opportunities ahead, NIH has recently launched a working group on AI to explore ways to make the best use of existing data, and harness the potential of artificial intelligence and machine learning to advance biomedical research and the practice of medicine.
Meanwhile, more and more impressive NIH-supported research featuring AI is being published. In my next blog, I’ll highlight a recent paper that uses AI to make a real difference for cervical cancer, particularly in low resource settings.
Reference:
[1] Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY.
Nat Med. 2019 Jan;25(1):65-69.
Links:
Arrhythmia (National Heart, Lung, and Blood Institute/NIH)
Video: Artificial Intelligence: Collecting Data to Maximize Potential (NIH)
Andrew Ng (Palo Alto, CA)
NIH Support: National Heart, Lung, and Blood Institute
Wearable mHealth Device Detects Abnormal Heart Rhythms Earlier
Posted on by Dr. Francis Collins
Caption: Woman wearing a Zio patch
Credit: Adapted from JAMA Network Summary Video
As many as 6 million Americans experience a common type of irregular heartbeat, called atrial fibrillation (AFib), that can greatly increase their risk of stroke and heart failure [1]. There are several things that can be done to lower that risk, but the problem is that a lot of folks have no clue that their heart’s rhythm is out of whack!
So, what can we do to detect AFib and get people into treatment before it’s too late? New results from an NIH-funded study lend additional support to the idea that one answer may lie in wearable health technology: a wireless electrocardiogram (EKG) patch that can be used to monitor a person’s heart rate at home.
All of Us Needs All of You
Posted on by Dr. Francis Collins
I’ve got some exciting news to share with you: as of May 6, 2018, NIH’s All of Us Research Program is open to everyone living in the United States, age 18 and older. That means that you, along with your family and friends, can join with 1 million or more Americans from all walks of life to create an unprecedented research resource that will speed biomedical breakthroughs and transform medicine.
To launch this historic undertaking, All of Us yesterday held community events at seven sites across the nation, from Alabama to Washington state. I took part in an inspiring gathering at the Abyssinian Baptist Church in New York’s Harlem neighborhood, where I listened to community members talk about how important it is for everyone to be able to take part in this research. I shared information on how All of Us will help researchers devise new ways of improving the health of everyone in this great nation.
Wearable Scanner Tracks Brain Activity While Body Moves
Posted on by Dr. Francis Collins
Credit: Wellcome Centre for Human Neuroimaging, University College London.
In recent years, researchers fueled by the BRAIN Initiative and many other NIH-supported efforts have made remarkable progress in mapping the human brain in all its amazing complexity. Now, a powerful new imaging technology promises to further transform our understanding [1]. This wearable scanner, for the first time, enables researchers to track neural activity in people in real-time as they do ordinary things—be it drinking tea, typing on a keyboard, talking to a friend, or even playing paddle ball.
This new so-called magnetoencephalography (MEG) brain scanner, which looks like a futuristic cross between a helmet and a hockey mask, is equipped with specialized “quantum” sensors. When placed directly on the scalp surface, these new MEG scanners can detect weak magnetic fields generated by electrical activity in the brain. While current brain scanners weigh in at nearly 1,000 pounds and require people to come to a special facility and remain absolutely still, the new system weighs less than 2 pounds and is capable of generating 3D images even when a person is making motions.
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