Device is latest example that wearable healthcare devices are moving past simple biomarker monitoring and into the area of assisting in rehab
Companies unrelated to traditional clinical laboratory medicine continue to develop wearable devices that enable individuals to monitor their health while also alerting physicians and caregivers in real time when certain biomarkers are out of range.
One recent example is US biotechnology company STAT Health Informatics in Boston, which has developed a wearable device that monitors blood flow to the ear and face “to better understand symptoms such as dizziness, brain fog, headaches, fainting, and fatigue that occur upon standing,” according to a press release. The tiny device is worn in the ear and connects wirelessly to a smartphone app.
Johns Hopkins University clinically tested the STAT device, and according to Medical Device Network, “It can predict a person fainting minutes before it happens and can be worn with more than 90% of devices that go in or around the ear. It can also be left in while sleeping and showering, meaning less likelihood of removing the device and forgetting to replace it.”
Another notable aspect of this invention is that it’s an example of how the ongoing miniaturization of various technologies makes it possible to invent smaller devices but with greater capabilities. In the case of the STAT device, it combines tiny sensors, Bluetooth, and an equally tiny battery to produce a device that fits in the ear and can function for up to three days before needing a recharge.
It’s easy to imagine these technologies being used for other types of diagnostic testing devices that could be managed by clinical laboratories.
“It’s well understood that the ear is a biometric gold mine because of its close proximity to the brain and major arteries. This allows for new biometrics … to be possible,” said Daniel Lee (above), co-founder and CEO of STAT Health, in a press release. “In addition, the ear is largely isolated from data corruption caused by arm motion—a problem that plagues current wearables and prevents them from monitoring heart metrics during many daily tasks. The ear is really the ideal window into the brain and heart.” Clinical laboratory managers may want to watch how this technology is further developed to incorporate other biomarkers for diseases and health conditions. (Photo copyright: STAT Health.)
How STAT Works
Every time the wearer stands, the STAT device tracks the change in response of blood pressure, heart rate, and blood flow to the head. “The device distills all this information into an ‘Up Score’ to track time spent upright. Its ‘Flow Score’ helps users pace their recovery by watching for blood flow abnormalities,” MassDevice reported.
According to the company’s website, STAT is intended for use in individuals who have been diagnosed with conditions known to suffer from drops in blood flow to the head, such as:
As an individual continues to use the device, STAT “learns about each user’s unique body to provide personalized coaching for healthy lifestyle choices,” MassDevice reported.
Another key factor is the technology built into the device. An optical sensor was chosen over ultrasound because STAT Health felt it was both easy to use and provided precise measurements accessing the shallow ear artery, MassDevice reported.
“Despite its small scale, the device incorporates advanced optical sensors, an accelerometer, a pressure sensor, temperature sensors, artificial intelligence (AI)-edge computing, three-day battery life (or more), and a micro solar panel,” Medical Device Network noted.
STAT’s image above demonstrates how truly minute the company’s wearable device is, even though it monitors blood flow to the face and ear looking for signs that the wearer is about to suffer bouts of dizziness or lightheadedness due to a drop in blood flow. (Photo copyright: STAT Health Informatics Inc.)
STAT’s Impact on Users’ Health
STAT’s developers intend the device to help individuals stay on track with their health. “The target population can navigate their condition better. If they’re not standing when they can, they will become deconditioned. This product encourages standing and being upright where possible, as part of rehab,” Lee told Medical Device Network.
Lee has been developing wearable in-ear devices for many years.
“Nobody has realized the ear’s true potential due to the miniaturization and complex systems design needed to make a practical and user-friendly ear wearable,” he told MassDevice. “After multiple engineering breakthroughs, we’ve succeeded in unlocking the ear to combine the convenience and long-term nature of wearables with the high fidelity nature of obtrusive clinical monitors. No other device comes close along the axis of wearability and cardiac signal quality, which is why we believe STAT is truly the world’s most advanced wearable.”
For clinical laboratories, though STAT is not a diagnostic test, it is the latest example of how companies are developing wearable monitoring devices intended to allow individuals to monitor their health. It moves beyond the simple monitoring of Apple Watch and Fitbit. This device can aid individuals during rehab.
Wearable healthcare devices will continue to be introduced that are smaller, allow more precise measurements of target biomarkers, and alert wearers in real time when those markers are out of range. Keeping in tune with the newest developments will help clinical laboratories and pathologists find new ways to support healthcare providers who recommend these devices for monitoring their patients conditions.
International research team that developed swarm learning believe it could ‘significantly promote and accelerate collaboration and information exchange in research, especially in the field of medicine’
“Swarm Learning” is a technology that enables cross-site analysis of population health data while maintaining patient privacy protocols to generate improvements in precision medicine. That’s the goal described by an international team of scientists who used this approach to develop artificial intelligence (AI) algorithms that seek out and identify lung disease, blood cancer, and COVID-19 data stored in disparate databases.
Since 80% of patient records feature clinical laboratory test results, there’s no doubt this protected health information (PHI) would be curated by the swarm learning algorithms.
In their study they wrote, “Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. … However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking, and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning.”
What is Swarm Learning?
Swarm Learning is a way to collaborate and share medical research toward a goal of advancing precision medicine, the researchers stated.
The technology blends AI with blockchain-based peer-to-peer networking to create information exchange across a network, the DZNE news release explained. The machine learning algorithms are “trained” to detect data patterns “and recognize the learned patterns in other data as well,” the news release noted.
“Medical research data are a treasure. They can play a decisive role in developing personalized therapies that are tailored to each individual more precisely than conventional treatments,” said Joachim Schultze, MD (above), Director, Systems Medicine at DZNE and Professor, Life and Medical Sciences Institute at the University of Bonn, in the news release. “It’s critical for science to be able to use such data as comprehensively and from as many sources as possible,” he added. This, of course, would include clinical laboratory test results data. (Photo copyright: University of Bonn.)
Since, as Dark Daily has reported many times, clinical laboratory test data comprises as much as 80% of patients’ medical records, such a treasure trove of information will most likely include medical laboratory test data as well as reports on patient diagnoses, demographics, and medical history. Swarm learning incorporating laboratory test results may inform medical researchers in their population health analyses.
“The key is that all participants can learn from each other without the need of sharing confidential information,” said Eng Lim Goh, PhD, Senior Vice President and Chief Technology Officer for AI at Hewlett Packard Enterprise (HPE), which developed base technology for swarm learning, according to the news release.
An HPE blog post notes that “Using swarm learning, the hospital can combine its data with that of hospitals serving different demographics in other regions and then use a private blockchain to learn from a global average, or parameter, of results—without sharing actual patient information.
“Under this model,” the blog continues, “‘each hospital is able to predict, with accuracy and with reduced bias, as though [it has] collected all the patient data globally in one place and learned from it,’ Goh says.”
Swarm Learning Applied in Study
The researchers studied four infectious and non-infectious diseases:
They used 16,400 transcriptomes from 127 clinical studies and assessed 95,000 X-ray images.
Data for transcriptomes were distributed over three to 32 blockchain nodes and across three nodes for X-rays.
The researchers “fed their algorithms with subsets of the respective data set” (such as those coming from people with disease versus healthy individuals), the news release noted.
Findings included:
90% algorithm accuracy in reporting on healthy people versus those diagnosed with diseases for transcriptomes.
76% to 86% algorithm accuracy in reporting of X-ray data.
Methodology worked best for leukemia.
Accuracy also was “very high” for tuberculosis and COVID-19.
X-ray data accuracy rate was lower, researchers said, due to less available data or image quality.
“Our study thus proves that swarm learning can be successfully applied to very different data. In principle, this applies to any type of information for which pattern recognition by means of artificial intelligence is useful. Be it genome data, X-ray images, data from brain imaging, or other complex data,” Schultze said in the DZNE news release.
The scientists say hospitals as well as research institutions may join or form swarms. So, hospital-based medical laboratory leaders and pathology groups may have an opportunity to contribute to swarm learning. According to Schultze, sharing information can go a long way toward “making the wealth of experience in medicine more accessible worldwide.”
