Know Labs Publishes Clinical Results in Leading Diabetes Journal

Rhea-AI Summary

Know Labs announced the publication of its study in the Diabetes Technology & Therapeutics Journal, demonstrating the accuracy of a non-invasive glycemic status screening device. The study showed that their RF dielectric sensor and machine learning algorithms accurately classified glycemic status with 93.37% accuracy compared to venous blood glucose values.

This proof-of-concept device has potential applications for early diabetes diagnosis, especially in underserved global populations. The study involved 31 participants aged 18-65 with prediabetes or Type 2 diabetes, with data collected through an Oral Glucose Tolerance Test and a control session. Results support the efficacy of the RF sensor, though further research is needed to improve sensitivity and specificity, particularly for hypoglycemic states.

Efforts to expand the application beyond proof-of-concept will be led by Chris Somogyi, alongside potential strategic partners, while the company continues to focus on FDA clearance for its non-invasive continuous glucose monitor.

Positive

• Study shows 93.37% accuracy in glycemic status classification.
• RF dielectric sensor accurately classifies hyperglycemic and normoglycemic states with sensitivities of 96.63% and 85.51%, respectively.
• None of the hyperglycemic values were categorized as hypoglycemia, and vice versa.
• Potential for early diabetes diagnosis in underserved global populations.
• Efforts underway to expand application beyond proof-of-concept through strategic partnerships.

Negative

• Further research needed to improve sensitivity and specificity for hypoglycemic states.

Insights

Financial Analyst

Know Labs has reported promising results in its recent clinical study for a non-invasive glycemic status screening device, indicating a potential new market segment for the company. The study illustrates a 93.37% accuracy rate for classifying glycemic statuses, which could significantly enhance the company’s value proposition in the diabetes management market. This news is relevant for potential investors, as it indicates a new application for Know Labs' technology beyond its core product, the non-invasive continuous glucose monitor.

From a financial perspective, this expansion could open up new revenue streams, especially considering the large global diabetes market, estimated at over $760 billion annually. Successful deployment of this screening device in low and middle-income countries could be particularly impactful, offering significant growth potential. Nevertheless, it’s essential to consider the costs associated with further research, regulatory approvals and commercialization efforts.

In the short term, the publication of these results in a peer-reviewed journal can boost investor confidence and potentially increase stock value due to positive market sentiment. However, long-term success will depend on the device's regulatory approval, market adoption and competitive positioning. Investors should monitor subsequent clinical trials, partnerships and regulatory milestones to assess the viability and profitability of this new device.

Medical Research Analyst

The publication of clinical results for Know Labs' non-invasive glycemic status screening device presents an exciting development in the medical field. The study’s findings, showing an accuracy rate of 93.37%, underscore the device's potential to classify glycemic status accurately without the need for invasive blood draws. This advancement could be particularly beneficial in early diabetes detection, allowing for timely intervention and better management of the disease.

The use of radiofrequency (RF) technology coupled with machine learning (ML) algorithms is innovative and could set a new standard in diabetes diagnostics. The study's methodology, involving multiple sessions and real-time data collection, adds to the credibility of the results. However, further research is necessary to refine the technology, especially in accurately detecting hypoglycemic events, which remains a critical challenge.

This device’s non-invasive nature could significantly enhance patient compliance and comfort, reducing the burden of frequent blood tests. Its potential application in low and middle-income countries could also democratize access to diabetes care, aligning with global health initiatives aimed at reducing healthcare disparities.

Study demonstrates accuracy for a proof-of–concept non-invasive glycemic status screening device.

SEATTLE--(BUSINESS WIRE)-- Know Labs, Inc. (NYSE American: KNW), a leading developer of non-invasive medical diagnostic technology, today announced the publication of its peer-reviewed study in Diabetes Technology & Therapeutics Journal titled, “A Glycemic Status Classification Model Using a Radiofrequency Noninvasive Blood Glucose Monitor.” Diabetes Technology & Therapeutics is a leading, peer-reviewed journal covering all aspects of diagnosing and managing diabetes with cutting-edge devices, drugs, drug delivery systems, and software.

The published clinical research results demonstrate that Know Labs’ proprietary non-invasive radiofrequency (RF) dielectric sensor and trade-secret machine learning (ML) algorithms correctly classified an individual’s glycemic status as hyperglycemic, normoglycemic, or hypoglycemic with 93.37% accuracy compared to venous blood glucose values–serving as an early proof-of-concept for a novel, non-invasive diabetes screening device.

Today, more than 500 million people worldwide are living with diabetes, with 75% residing in low and middle-income countries and an estimated 240 million people worldwide remaining undiagnosed. Expanding the potential application of the recently announced KnowU™ beyond non-invasive continuous blood glucose monitoring, the non-invasive screening device could support underserved global populations by facilitating early identification and intervention—potentially reducing diabetes-related hospitalizations and increasing access globally.

“Early diagnosis and intervention for diabetes are critical to both improving patient outcomes and enabling healthcare systems to allocate resources more economically and efficiently,” said Ron Erickson, CEO and Chairman at Know Labs. “This proof-of-concept for the use of our novel RF sensor as a glycemic status screening tool indicates the device’s potential to help funnel previously undiagnosed patients more effectively into the healthcare system.”

Study Design

The study included 31 participants aged 18-65 with prediabetes or Type 2 diabetes. Know Labs’ RF sensor continuously scanned participants' forearms for up to two, three-hour sessions each during a 75g Oral Glucose Tolerance Test, and a third session in which water was given instead of liquid glucose to act as a control. Concurrently, venous blood draws were taken every five minutes and measured with an FDA-cleared glucose hospital meter system to create 2,637 paired observations. Data was preprocessed using smoothing techniques and an 80/20 split was performed to create model training and test datasets, respectively. Know Labs trained a Time Series Forest ML model to estimate reference venous blood glucose values on 80% of the data consisting of 2,109 paired RF device and venous blood glucose values randomly selected from the total dataset and then tested on the remaining, held-out 20% (528 paired values).

Results

The findings show that from the total test dataset of 528 paired values, the model correctly classified glycemic status 93.37% of the time as hyperglycemic, normoglycemic, or hypoglycemic. The model achieved sensitivities of 96.63% and 85.51% for normoglycemic and hyperglycemic classes, respectively. Specificities were 84.51% and 96.92%. More data is required in the hypoglycemic range to evaluate sensitivity and specificity in that glycemic class. Importantly, none of the hyperglycemic values were categorized as hypoglycemia, and none of the hypoglycemic values were categorized as hyperglycemia.

The results support the accuracy of Know Lab’s proprietary non-invasive RF dielectric sensor and ML techniques for glycemic status classification. Further research is needed to enrich the dataset for categorical screening and improve the accuracy and sensitivity of each glycemic status.

Efforts led by President, International, Chris Somogyi, will aim to expand this application beyond proof-of-concept alongside potential strategic partners for a Rest of the World (RoW) product that exploits Know Labs’ proprietary RF technology for use as a screening device. This will occur in parallel, as the Company maintains its core focus on bringing the first FDA-cleared non-invasive continuous glucose monitor to the marketplace.

For more information on Know Labs, visit www.knowlabs.co.

About Know Labs, Inc.

Know Labs, Inc. is a public company whose shares trade on the NYSE American Exchange under the stock symbol “KNW.” The Company’s platform technology uses spectroscopy to direct electromagnetic energy through a substance or material to capture a unique molecular signature. The technology can be integrated into a variety of wearable, mobile or bench-top form factors. This patented and patent-pending technology makes it possible to effectively identify and monitor analytes that could only previously be performed by invasive and/or expensive and time-consuming lab-based tests. The first application of the technology will be in a product marketed as a non-invasive glucose monitor. The device will provide the user with accessible and affordable real-time information on blood glucose levels. This product will require U.S. Food and Drug Administration clearance prior to its introduction to the market.

Safe Harbor Statement

This release contains statements that constitute forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995 and Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These statements appear in a number of places in this release and include all statements that are not statements of historical fact regarding the intent, belief or current expectations of Know Labs, Inc., its directors or its officers with respect to, among other things: (i) financing plans; (ii) trends affecting its financial condition or results of operations; (iii) growth strategy and operating strategy; and (iv) performance of products. You can identify these statements by the use of the words “may,” “will,” “could,” “should,” “would,” “plans,” “expects,” “anticipates,” “continue,” “estimate,” “project,” “intend,” “likely,” “forecast,” “probable,” “potential,” and similar expressions and variations thereof are intended to identify forward-looking statements. Investors are cautioned that any such forward-looking statements are not guarantees of future performance and involve risks and uncertainties, many of which are beyond Know Labs, Inc.’s ability to control, and actual results may differ materially from those projected in the forward-looking statements as a result of various factors. These risks and uncertainties also include such additional risk factors as are discussed in the Company’s filings with the U.S. Securities and Exchange Commission, including its Annual Report on Form 10-K for the fiscal year ended September 30, 2023, Forms 10-Q and 8-K, and in other filings we make with the Securities and Exchange Commission from time to time. These documents are available on the SEC Filings section of the Investor Relations section of our website at www.knowlabs.co. The Company cautions readers not to place undue reliance upon any such forward-looking statements, which speak only as of the date made. The Company undertakes no obligation to update any forward-looking statement to reflect events or circumstances after the date on which such statement is made.

View source version on businesswire.com: https://www.businesswire.com/news/home/20240711542396/en/

For Know Labs Media Inquiries Contact:

Matter Health

Abby Mayo

Knowlabs@matternow.com

Ph. (617) 272-0592

Know Labs, Inc. Contact:

Jess English

jess@knowlabs.co

Ph. (646) 912-2024

Source: Know Labs, Inc.

FAQ

What are the results of Know Labs' non-invasive glycemic status screening device study?

The study showed that Know Labs' RF dielectric sensor and machine learning algorithms classified glycemic status with 93.37% accuracy compared to venous blood glucose values.

What was the accuracy of Know Labs' RF sensor in classifying hyperglycemic and normoglycemic states?

The RF sensor achieved sensitivities of 96.63% for normoglycemic and 85.51% for hyperglycemic states.

What are the implications of Know Labs' study for diabetes diagnosis?

The device could support early diagnosis and intervention for diabetes, especially in underserved global populations, potentially reducing hospitalizations and increasing access to healthcare.

What were the specificities achieved by Know Labs' RF sensor in the study?

The specificities were 84.51% for normoglycemic and 96.92% for hyperglycemic states.

What further research is needed for Know Labs' non-invasive glycemic status screening device?

More data is required to evaluate sensitivity and specificity in the hypoglycemic range, and further research is needed to enrich the dataset for categorical screening.

What is the next step for Know Labs' non-invasive glycemic status screening device?

Know Labs will continue to expand the application beyond proof-of-concept through strategic partnerships and focus on obtaining FDA clearance for its non-invasive continuous glucose monitor.