Not the first smart diaper to come along, but consumers seem unready for diapers that can flag urinary tract infections and other biomarkers usually tested by clinical laboratories
Will wonders never cease? For centuries, parents had only their own senses to determine when infants needed diaper changing. Today, however, caregivers can rely on “smart diapers” to send alerts when a diaper is soiled. Crying, smelly babies may no longer be the gold standard in diaper management. But are smart diapers practical?
The sensor array is “so cheap and simple” it “could clear the way for wearable, self-powered health monitors for use not only in ‘smart diapers’ but also to predict major health concerns like cardiac arrest and pneumonia,” a Penn State new release noted.
However, clinical laboratory managers following similar developments probably know that this is not the first scientific effort to develop a smart diaper that uses some type of sensor to detect a biomarker and issue an alert to the wearer or caregivers.
“Our team has been focused on developing devices that can capture vital information for human health,” said Huanyu “Larry” Cheng, PhD (above), the James L. Henderson, Jr. Memorial Associate Professor of Engineering Science and Mechanics at Penn State in a news release. “The goal is early prediction for disease conditions and health situations, to spot problems before it is too late.” This is yet another example of how researchers are working to take more testing out of clinical laboratories and offer unique assays that can be used as wearables—whether as a diaper, a skin patch, or a smart watch. (Photo copyright: Penn State University.)
This Smart Diaper Is as Simple to Use as Paper and Pencil
The Penn State sensor array takes advantage of how paper naturally reacts to wetness and utilizes the graphite in pencil marking to interact with the water molecules and sodium chloride.
Once the water molecules are absorbed by the paper, the sodium chloride solution becomes ionized and electrons start to stream towards the graphite. This movement sets off the sensor, which is extremely sensitive to humidity. According to the study, the sensor can provide accurate readings over a wide range of humidity levels, from 5.6% to 90%.
“We wanted to develop something low-cost that people would understand how to make and use, and you can’t get more accessible than pencil and paper,” said Li Yang, PhD, a professor in the School of Artificial Intelligence at China’s Hebei University of Technology and one of the authors of the study, in the Penn State news release.
“You don’t need to have some piece of multi-million-dollar equipment for fabrication. You just need to be able to draw within the lines of a pre-drawn electrode on a treated piece of paper. It can be done simply and quickly.”
The diaper is connected to a tiny lithium battery. When the sensor recognizes an increase in humidity the battery powers transmission of the change to a smartphone via Bluetooth technology. This notification informs caregivers that it is time to change the diaper.
“That application was actually born out of personal experience,” explained Huanyu “Larry” Cheng, PhD, James L. Henderson, Jr. Memorial Associate Professor of Engineering Science and Mechanics at Penn State, one of the authors of the study and father to two young children. “There’s no easy way to know how wet is wet, and that information could be really valuable for parents. The sensor can provide data in the short-term, to alert for diaper changes, but also in the long-term, to show patterns that can inform parents about the overall health of their child.”
Do Consumers Want Smart Diapers?
Research into such wearable sensors has been gaining momentum in the scientific community as a novel way to detect and deal with several medical conditions. The Penn State team hopes that devices such as their smart diaper can be used in the future to alert caregivers about the overall health of their children and clients.
“Our team has been focused on developing devices that can capture vital information for human health,” Cheng said. “The goal is early prediction for disease conditions and health situations, to spot problems before it is too late.”
Previous research teams have had similar smart diaper goals.
However, these types of products have yet to gain significant popularity with consumers. Regardless, sales projections for smart diapers remain positive.
According to a MarketsandMarkets report, the smart diaper market, estimated to be $646 million (US) in 2021, is expected to surpass $1.5 billion by 2026. The demand for smart diapers, the report notes, is increasing due to:
Growing elderly populations,
Rising disposable incomes,
Increasing personal hygiene awareness,
Growing populations in emerging countries, and
Expanding preference for advanced technology when it comes to health.
So, it’s uncertain if consumers are now ready for a device in their baby’s diaper telling them it’s time for a change. Regardless, researchers will likely continue developing tools that combine new diagnostics with existing products to help people better understand and monitor their health and the health of their loved ones.
Meanwhile, clinical laboratory managers and pathologists can remain on the alert for future published studies and press releases announcing new wearable items containing sensors, such as smart diapers. The unanswered question is whether both consumers and healthcare professionals will consider these novel inventions useful devices in the care of young and old alike.
Fujifilm acquired Inspirata’s Dynamyx digital pathology technology and business while GE Healthcare announced a partnership with Tribun Health in Europe
Clinical pathology laboratories, especially in the US, have been slow to adopt digital imaging systems. But recent industry deals suggest that the market may soon heat up, at least in the eyes of vendors. These collaborators may hope that, by integrating diagnostic data, the accuracy and productivity of anatomic pathologists will improve while also shortening the time to diagnosis.
In the press release, Fujifilm stated that 85% of US healthcare organizations use analog systems for pathology. That compares with 86% in Europe and 90% in Asia, the company stated.
“Acquiring Inspirata’s digital pathology business allows Fujifilm to be an even stronger healthcare partner—bridging a technological gap between pathology, radiology, and oncology to facilitate a more collaborative approach to care delivery across the enterprise,” said Fujifilm CEO and president Teiichi Goto in the press release.
The press release cited data from Signify Research, a healthcare technology marketing data firm that is predicting the global market for digital pathology systems would double from $320 million in 2021 to $640 million by 2025.
Fujifilm previously had a deal with Inspirata to sell the Dynamyx system exclusively in the UK, Italy, Spain, Portugal, Belgium, the Netherlands, and Luxembourg, an August press release noted.
“A $320 million global industry in 2021 projected to reach $640 million by 2025, the rising number of cancer cases and the demonstrated benefits of digital pathology are fueling significant demand and market growth in the hospital and pharmaceutical industries,” said Henry Izawa (above), president and CEO, Fujifilm Healthcare Americas Corporation, in a press release. “These evolving clinical needs fuel Fujifilm’s investment and innovation in the digital revolution, and we look forward to introducing Dynamyx and its host of unique features and benefits to our Synapse customers and prospects as we strive to enable more efficient medical diagnosis and high-quality care.” (Photo copyright: LinkedIn.)
In announcing their new collaboration, GE Healthcare and Tribun Health said the integration of their systems—Edison Datalogue and the Tribun Health suite—would foster collaboration between pathologists and clinicians by providing a consolidated location for imaging records. This capability is especially important in oncology, they said.
“The oncology care pathway is one of the most complex with multiple steps involving a variety of specialists, complex tools, frequent decisions, and large data sets,” said GE Healthcare CEO of Enterprise Digital Solutions Nalinikanth Gollagunta in a GE press release. “With this digital pathology collaboration, we continue our journey towards simplifying the oncology care pathway with improved data management, the digitization of pathology, and streamlined data access.”
Tribun Health, based in Paris, France, offers a digital pathology platform that incorporates a camera system, artificial intelligence (AI)-based analysis, remote collaboration, and storage management, plus integration with third-party automation apps.
GE Healthcare claims that Edison Datalogue has the largest share of the Vendor Neutral Archive (VNA) market. That term refers to image archiving systems that use standard formats and interfaces instead of proprietary formats. They are an alternative to the more widely used Picture Archiving and Communications Systems (PACS) used in medical imaging.
The collaboration between the companies “is probably a strategic move to position GE as an integrator of imaging data and digital pathology data in oncology,” said Robert Michel Editor-in-Chief of Dark Daily and its sister publication The Dark Report.
GE’s History with Dynamyx
This is not GE Healthcare’s first foray into digital pathology. In fact, the company had a major hand in launching the very Dynamyx system that Fujifilm recently acquired.
In “GE Healthcare Sells Omnyx to Inspirata,” The Dark Report interviewed Inspirata CEO Satish Sanan who at that time said the acquisition would allow his company to offer “a fully integrated, end-to-end digital pathology solution” in Canada and Europe. But GE Healthcare chose to end the partnership in 2016, citing regulatory uncertainty and variable global demand. Two years later, GE sold Omnyx to Inspirata.
GE Healthcare’s new collaboration with Tribun Health shows that the company “still recognizes the value of the pathology data in cancer diagnosis and wants to be in a position to manage that digital pathology data,” Michel said.
Fujifilm’s Plans
Fujifilm said it will incorporate Dynamyx into its Synapse Enterprise Imaging suite, which includes VNA, Radiology PACS, and Cardiology PACS. “Future releases of Dynamyx will also create opportunities for Fujifilm to support pharmaceutical and contract research organizations with toxicity testing data management for drug development,” the company stated in the press release.
With its recent moves into digital pathology, Fujifilm will be taking on major competitors including Philips, Danaher, and Roche, MedTech Dive reported.
Clinical laboratories and point-of-care settings may have a new diagnostic test if this novel handheld device and related technology is validated by clinical trials
Efforts to develop breath analyzers that accurately identify viral infections, such as SARS-CoV-2 and Influenza, have been ongoing for years. The latest example is ViraWarn from Opteev Technologies in Baltimore, Maryland, and its success could lead to more follow-up PCR tests performed at clinical laboratories.
“Breath is one of the most appealing non-invasive sample types for diagnosis of infectious and non-infectious disease,” said Opteev in its FDA Pre-EUA application. “Exhaled breath is very easy to provide and is less prone to user errors. Breath contains a number of biomarkers associated with different ailments that include volatile organic compounds (VOCs), viruses, bacteria, antigens, and nucleic acid.”
Further clinical trials and the FDA Pre-EUA are needed before ViraWarn can be made available to consumers. In the meantime, Opteev announced that the CES (Consumer Electronic Show) had named ViraWarn as a 2023 Innovation Award Honoree in the digital health category.
“ViraWarn is designed to allow users an ultra-fast and convenient way to know if they are spreading a dangerous respiratory virus. With a continued increase in COVID-19 and a new surge in RSV and influenza cases, we’re eager to bring ViraWarn to market so consumers can easily blow into a personal device and find out if they are positive or negative,” said Conrad Bessemer (above), Opteev President and Co-Founder, in a news release.
Opteev is a subsidiary of Novatec, a supplier of machinery and sensor technology, and a sister company to Prophecy Sensorlytics, a wearable sensors company.
The ViraWarn breath analyzer uses a silk-based sensor that “traces the electric discharge of respiratory viruses coupled with an artificial intelligence (AI) processor to filter out any potential inaccuracies,” according to the news release.
Here is how the breath analyzer (mouthpiece, attached biosensor chamber, and attached printed circuit board chamber) is deployed by a user, according to the Opteev website:
The user turns on the device and an LED light indicates readiness.
The user blows twice into the mouthpiece.
A carbon filter stops bacteria and VOCs and allows virus particles to pass through.
As “charge carriers,” virus particles have a “cumulative charge.”
Electrical data are forwarded to the AI processor.
The AI processer delivers a result.
Within 60 seconds, a red signal indicates a positive presence of a virus and a green signal indicates negative one.
“The interaction of the virus with a specially designed liquid semiconductive medium, or a solid polymer semiconductor, generates changes in the conductivity of the electrical biosensor, which can then be picked up by electrodes. Such electrical data can be analyzed using algorithms and make a positive or negative call,” explains an Opteev white paper on the viral screening process.
While the ViraWarn breath analyzer can identify the presence of a virus, it cannot distinguish between specific viruses, the company noted. Therefore, a clinical laboratory PCR test is needed to confirm results.
Other Breath Tests
Opteev is not the only company developing diagnostic tests using breath samples.
For clinical laboratory managers and pathologists, Opteev’s ViraWarn is notable in breath diagnostics development because it is a personal hand-held tool. It empowers people to do self-tests and other disease screenings, all of which would need to be confirmed with medical laboratory testing in the case of positive results.
Further, it is important to understand that consumers are the primary target for this novel diagnostic device. This is consistent with investor-funding companies wanting to develop testing solutions that can be used by consumers. At the same time, a device like ViraWarn could be used by clinical laboratories in their patient service centers to provide rapid test results.
Google designed the suite to ease radiologists’ workload and enable easy and secure sharing of critical medical imaging; technology may eventually be adapted to pathologists’ workflow
Clinical laboratory and pathology group leaders know that Google is doing extensive research and development in the field of cancer diagnostics. For several years, the Silicon Valley giant has been focused on digital imaging and the use of artificial intelligence (AI) algorithms and machine learning to detect cancer.
Now, Google Cloud has announced it is launching a new medical imaging suite for radiologists that is aimed at making healthcare data for the diagnosis and care of cancer patients more accessible. The new suite “promises to make medical imaging data more interoperable and useful by leveraging artificial intelligence,” according to MedCity News.
In a press release, medical technology company Hologic, and healthcare provider Hackensack Meridian Health in New Jersey, announced they were the first customers to use Google Cloud’s new suite of medical imaging products.
“Hackensack Meridian Health has begun using it to detect metastasis in prostate cancer patients earlier, and Hologic is using it to strengthen its diagnostic platform that screens women for cervical cancer,” MedCity News reported.
“Google pioneered the use of AI and computer vision in Google Photos, Google Image Search, and Google Lens, and now we’re making our imaging expertise, tools, and technologies available for healthcare and life sciences enterprises,” said Alissa Hsu Lynch (above), Global Lead of Google Cloud’s MedTech Strategy and Solutions, in a press release. “Our Medical Imaging Suite shows what’s possible when tech and healthcare companies come together.” Clinical laboratory companies may find Google’s Medical Imaging Suite worth investigating. (Photo copyright: Influencive.)
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Easing the Burden on Radiologists
Clinical laboratory leaders and pathologists know that laboratory data drives most healthcare decision-making. And medical images make up 90% of all healthcare data, noted an article in Proceedings of the IEEE (Institute of Electrical and Electronics Engineers).
More importantly, medical images are growing in size and complexity. So, radiologists and medical researchers need a way to quickly interpret them and keep up with the increased workload, Google Cloud noted.
“The size and complexity of these images is huge, and, often, images stay sitting in data siloes across an organization,” said Alissa Hsu Lynch, Global Lead, MedTech Strategy and Solutions at Google, told MedCity News. “In order to make imaging data useful for AI, we have to address interoperability and standardization. This suite is designed to help healthcare organizations accelerate the development of AI so that they can enable faster, more accurate diagnosis and ease the burden for radiologists,” she added.
According to the press release, Google Cloud’s Medical Imaging Suite features include:
Imaging Storage: Easy and secure data exchange using the international DICOM (digital imaging and communications in medicine) standard for imaging. A fully managed, highly scalable, enterprise-grade development environment that includes automated DICOM de-identification. Seamless cloud data management via a cloud-native enterprise imaging PACS (picture archiving and communication system) in clinical use by radiologists.
Imaging Lab: AI-assisted annotation tools that help automate the highly manual and repetitive task of labeling medical images, and Google Cloud native integration with any DICOMweb viewer.
Imaging Datasets and Dashboards: Ability to view and search petabytes of imaging data to perform advanced analytics and create training datasets with zero operational overhead.
Imaging AI Pipelines: Accelerated development of AI pipelines to build scalable machine learning models, with 80% fewer lines of code required for custom modeling.
Imaging Deployment: Flexible options for cloud, on-prem (on-premises software), or edge deployment to allow organizations to meet diverse sovereignty, data security, and privacy requirements—while providing centralized management and policy enforcement with Google Distributed Cloud.
First Customers Deploy Suite
Hackensack Meridian Health hopes Google’s imaging suite will, eventually, enable the healthcare provider to predict factors affecting variance in prostate cancer outcomes.
“We are working toward building AI capabilities that will support image-based clinical diagnosis across a range of imaging and be an integral part of our clinical workflow,” said Sameer Sethi, Senior Vice President and Chief Data and Analytics Officer at Hackensack, in a news release.
The New Jersey healthcare network said in a statement that its work with Google Cloud includes use of AI and machine learning to enable notification of newborn congenital disorders and to predict sepsis risk in real-time.
Hologic, a medical technology company focused on women’s health, said its collaboration integrates Google Cloud AI with the company’s Genius Digital Diagnostics System.
“By complementing our expertise in diagnostics and AI with Google Cloud’s expertise in AI, we’re evolving our market-leading technologies to improve laboratory performance, healthcare provider decision making, and patient care,” said Michael Quick, Vice President of Research and Development and Innovation at Hologic, in the press release.
Hologic says its Genius Digital Diagnostics System combines AI with volumetric medical imaging to find pre-cancerous lesions and cancer cells. From a Pap test digital image, the system narrows “tens of thousands of cells down to an AI-generated gallery of the most diagnostically relevant,” according to the company website.
Hologic plans to work with Google Cloud on storage and “to improve diagnostic accuracy for those cancer images,” Hsu Lynch told MedCity News.
Medical image storage and sharing technologies like Google Cloud’s Medical Imaging Suite provide an opportunity for radiologists, researchers, and others to share critical image studies with anatomic pathologists and physicians providing care to cancer patients.
One key observation is that the primary function of this service that Google has begun to deploy is to aid in radiology workflow and productivity, and to improve the accuracy of cancer diagnoses by radiologists. Meanwhile, Google continues to employ pathologists within its medical imaging research and development teams.
Assuming that the first radiologists find the Google suite of tools effective in support of patient care, it may not be too long before Google moves to introduce an imaging suite of tools designed to aid the workflow of surgical pathologists as well.
And in less than eight hours, they had diagnosed a child with a rare genetic disorder, results that would take clinical laboratory testing weeks to return, demonstrating the clinical value of the genomic process
In another major genetic sequencing advancement, scientists at Stanford University School of Medicine have developed a method for rapid sequencing of patients’ whole human genome in as little as five hours. And the researchers used their breakthrough to diagnose rare genetic diseases in under eight hours, according to a Stanford Medicine news release. Their new “ultra-rapid genome sequencing approach” could lead to significantly faster diagnostics and improved clinical laboratory treatments for cancer and other diseases.
“A few weeks is what most clinicians call ‘rapid’ when it comes to sequencing a patient’s genome and returning results,” said cardiovascular disease specialist Euan Ashley, MD, PhD (above), professor of medicine, genetics, and biomedical data science, at Stanford University in the news release. “The right people suddenly came together to achieve something amazing. We really felt like we were approaching a new frontier.” Their results could lead to faster diagnostics and clinical laboratory treatments. (Photo copyright: Stanford Medicine.)
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Need for Fast Genetic Diagnosis
In their NEJM paper, the Stanford scientists argue that rapid genetic diagnosis is key to clinical management, improved prognosis, and critical care cost savings.
“Although most critical care decisions must be made in hours, traditional testing requires weeks and rapid testing requires days. We have found that nanopore genome sequencing can accurately and rapidly provide genetic diagnoses,” the authors wrote.
To complete their study, the researchers sequenced the genomes of 12 patients from two hospitals in Stanford, Calif. They used nanopore genome sequencing, cloud computing-based bioinformatics, and a “custom variant prioritization.”
Their findings included:
Five people received a genetic diagnosis from the sequencing information in about eight hours.
Diagnostic rate of 42%, about 12% higher than the average rate for diagnosis of genetic disorders (the researchers noted that not all conditions are genetically based and appropriate for sequencing).
Five hours and two minutes to sequence a patient’s genome in one case.
Seven hours and 18 minutes to sequence and diagnose that case.
How the Nanopore Process Works
To advance sequencing speed, the researchers used equipment by Oxford Nanopore Technologies with 48 sequencing units called “flow cells”—enough to sequence a person’s whole genome at one time.
The Oxford Nanopore PromethION Flow Cell generates more than 100 gigabases of data per hour, AI Time Journal reported. The team used a cloud-based storage system to enable computational power for real-time analysis of the data. AI algorithms scanned the genetic code for errors and compared the patients’ gene variants to variants associated with diseases found in research data, Stanford explained.
According to an NVIDIA blog post, “The researchers accelerated both base calling and variant calling using NVIDIA GPUs on Google Cloud. Variant calling, the process of identifying the millions of variants in a genome, was also sped up with NVIDIA Clara Parabricks, a computational genomics application framework.”
Rapid Genetic Test Produces Clinical Benefits
“Together with our collaborators and some of the world’s leaders in genomics, we were able to develop a rapid sequencing analysis workflow that has already shown tangible clinical benefits,” said Mehrzad Samadi, PhD, NVIDIA Senior Engineering Manager and co-author of the NEJM paper, in the blog post. “These are the kinds of high-impact problems we live to solve.”
In their paper, the Stanford researchers described their use of the rapid genetic test to diagnose and treat an infant who was experiencing epileptic seizures on arrival to Stanford’s pediatric emergency department. In just eight hours, their diagnostic test found that the infant’s convulsions were attributed to a mutation in the gene CSNK2B, “a variant and gene known to cause a neurodevelopmental disorder with early-onset epilepsy,” the researchers wrote.
“By accelerating every step of this process—from collecting a blood sample to sequencing the whole genome to identifying variants linked to diseases—[the Stanford] research team took just hours to find a pathogenic variant and make a definitive diagnosis in a three-month-old infant with a rare seizure-causing genetic disorder. A traditional gene panel analysis ordered at the same time took two weeks to return results,” AI Time Journal reported.
New Benchmarks
The Stanford research team wants to cut the sequencing time in half. But for now, the five-hour rapid whole genome sequence can be considered by clinical laboratory leaders, pathologists, and research scientists a new benchmark in genetic sequencing for diagnostic purposes.
Stories like Stanford’s rapid diagnosis of the three-month old patient with epileptic seizures, point to the ultimate value of advances in genomic sequencing technologies.
