Clinical Research &
Development

A study titled, “Non-Invasive Blood Glucose Monitoring in People with Diabetes Using an RF Sensor and Venous Blood Comparator,” assesses the accuracy of the novel Know Labs radiofrequency (RF) dielectric sensor for non-invasive blood glucose measurement in participants with prediabetes and Type 2 diabetes using venous blood as comparative reference. Results were presented as e-Poster at the 17th International Conference on Advanced Technologies and Treatments for Diabetes.

A study titled, “Novel Data Preprocessing Techniques in an Expanded Dataset Improve Machine Learning Model Accuracy for a Non-Invasive Blood Glucose Monitor,” validates the stability of a machine learning model on an expanded dataset.

A study titled, “Algorithm Refinement in the Non-Invasive Detection of Blood Glucose Using Know Labs' Bio-RFID Technology,” demonstrates an ML model and other data science techniques improved the accuracy of Bio-RFID for predicting blood glucose, using the Dexcom G6® as reference device.

A study titled, “Technical Feasibility of a Novel Sensor for Non-Invasive Blood Glucose Monitoring Compared to Dexcom G6®,” demonstrates that the Bio-RFID sensor can deliver stable, repeatable results in predicting blood glucose concentrations using the Dexcom G6® as a reference device. Results were presented at American Association of Clinical Endocrinology (AACE) 2023 Annual Meeting.

A study titled, “Detecting Unique Analyte-Specific Radio Frequency Spectral Responses in Liquid Solutions – Implications for Non-Invasive Physiologic Monitoring,” was conducted in collaboration with Mayo Clinic and demonstrates the accuracy of Know Labs’ non-invasive Bio-RFID technology in quantifying different analytes in vitro. Results were presented at the American Physiological Society 2023 Summit.

This technical report titled, "Non-Invasive Blood Glucose Monitoring: A Validation of a Novel Sensor Compared to a Dexcom G6®," presents proof of concept for a new method to quantify blood glucose levels (BGL) in vivo non-invasively using RF methods by means of training a model to predict readings of the Dexcom G6®, as a proxy for BGL. The method uses a new type of sensing device that rapidly scans a wide band of RF frequencies. The report outlines data science techniques used to train a neural network model to make predictions, and includes metrics of model success and future directions of this work.

This exploratory study was ongoing, in parallel to blood glucose clinical research, and provided a proof-of-concept that Know Labs’ technology has the potential to accurately identify and monitor blood oxygen saturation levels.

This exploratory study was conducted in August 2021 and provided the first indication that Know Labs’ technology could be an accurate, cost-effective, and non-invasive alternative to the current FDA-cleared glucose monitoring devices on the market.