Know Labs’ Non-Invasive Glucose Monitoring Technology Shows Improved Accuracy

Latest study demonstrates a machine learning model improved Bio-RFID™ sensor’s accuracy for predicting blood glucose, using Dexcom G6® as reference device

Know Labs, Inc. (NYSE American: KNW) today announced the results of a new study titled, “Algorithm Refinement in the Non-Invasive Detection of Blood Glucose Using Know Labs’ Bio-RFID Technology.” The study demonstrates that algorithm optimization using a light gradient-boosting machine (lightGBM) machine learning model improved the accuracy of Know Labs’ Bio-RFID™ sensor technology at quantifying blood glucose, demonstrating an overall Mean Absolute Relative Difference (MARD) of 12.9% – which is within the range of FDA-cleared blood glucose monitoring devices. Bio-RFID is a novel technology platform that uses electromagnetic energy in the form of radio waves to non-invasively capture molecular signatures and convert them into meaningful information.

Like all previous Know Labs clinical studies, this study was designed to assess the ability of the Bio-RFID sensor to non-invasively and continuously quantify blood glucose, using the Dexcom G6® continuous glucose monitor (CGM) as a proxy for the measurement of blood glucose. Unique from previous studies, Know Labs tested new data science techniques and trained a lightGBM model to predict blood glucose using 1,555 observations – or reference device values – from over 130 hours of data collection across five healthy participants. Using this model, Know Labs was able to predict blood glucose in the test set – the dataset that provides a blind evaluation of model performance – with a MARD of 12.7% in the normoglycemic range and 14.0% in the hyperglycemic range.

“This is a transformational time for Know Labs. We are constantly uncovering new learnings in our research, and in this case found that the lightGBM model is well-suited for these early datasets given the amount of data available,” said Steve Kent, Chief Product Officer at Know Labs. “In our previous technical feasibility study we utilized a neural network, and as is best practice when developing algorithms, our data science team is constantly refining our machine learning models to understand and optimize system performance and accuracy. This positive development is another critical step in our data collection, algorithm refinement, and technical development.”

This study, which was peer-reviewed by Know Labs’ Scientific Advisory Board, builds upon recently released peer-reviewed research. In February, Know Labs published a proof-of-concept study that examined the efficacy of the Bio-RFID sensor using one participant, resulting in a MARD of 19.3%. Earlier this month, Know Labs also released study results validating the technical feasibility of Bio-RFID using a neural network (NN) model to predict readings of the Dexcom G6® as a proxy for blood glucose, which resulted in a MARD of 20.6%. The techniques used to analyze the data differed from previous analyses among the same (N=5) participant population, including: approach to feature reduction, stratification of the data by glycemic range and only from the arm corresponding to the reference device, and a different machine learning model. The improved accuracy as measured by a MARD of 12.9% achieved in

this study is comparable to other independently validated MARD values reported for today’s FDA-cleared, commercially available CGM devices.

“A MARD of 12.9% at this stage in our development is a truly remarkable feat. Our whole team is thrilled by these findings and the improved accuracy of our Bio-RFID technology as we continue to refine our approach,” said Ron Erickson, CEO and Chairman at Know Labs. “Our goal with these ongoing clinical studies is to develop large volumes of data to enable further model development, which is a critical step in our goal to bring the first FDA-cleared non-invasive glucose monitoring device to the market so that millions of people can manage their diabetes more efficiently.”

The full manuscript of this study will be submitted to a peer-review journal as Know Labs continues to prioritize external validation of the Bio-RFID technology. To view Know Labs’ growing body of peer-reviewed research, visit www.knowlabs.co/research-and-validation.

 

SourceKnow Labs
Medical Device News Magazinehttps://infomeddnews.com
Medical Device News Magazine provides our readership with breaking medical device / biotechnology news. Our subscribers include medical specialists, device industry executives, investors, and other allied health professionals, as well as patients who are interested in researching various medical devices. We hope you find value in our easy-to-read publication and its overall objectives! Medical Device News Magazine is a division of PTM Healthcare Marketing, Inc. Pauline T. Mayer is the managing editor.

More News!

“It’s exciting to be one of the first two hospitals in Europe to use Stryker’s Mixed Reality Guidance System,” said Professor Berhouet. “I am also pleased to be leading a pilot study to investigate the safety and effectiveness of this new technology, alongside three other centres in France.”
The KnowU incorporates the sensor that the Company plans to submit to the FDA for clearance. This proprietary sensor has been tested and proven stable and accurate in the lab setting. It was included in the Company’s prototype to validate stability outside of the lab, and is now miniaturized and wearable.
The ArthroFree system is the first FDA-cleared wireless camera for arthroscopy and general endoscopy, both areas of minimally invasive surgery. The system is designed to help surgeons work with aximum dexterity and focus.
The project, which was led by the University of Southern California, included large increases in representation among men of African, Hispanic and Asian ancestries, that were contributed in part by an ongoing collaboration between the U.S. Department of Veterans Affairs and DOE reports Argonne.
This innovative software turns smartphones into medical-grade stethoscopes, allowing people to capture, analyze, and share critical heart health data with medical personnel from the comfort of any location notes Sparrow BioAcoustics.

By using this website you agree to accept Medical Device News Magazine Privacy Policy