Algorithm Refinement in the Non-Invasive Detection of Blood Glucose Using Know Labs Bio-RFID Technology

Diabetes Mellitus (DM) is a highly prevalent and increasingly common disorder that can have dire health consequences if not properly managed. Management of DM involves monitoring of blood glucose levels which can be both cumbersome and invasive, limiting adherence. We present a validation for a novel sensor designed to measure blood glucose (BG) non-invasively using Radio Frequency (RF) waves. In this n=5 study, we trained a Light Gradient-Boosting Machine (lightGBM) model to predict BG values using 1,555 observations from over 130 hours of data collection from 5 participants. An observation is defined as data collected from 13 Bio-RFID sensor sweeps paired with a single Dexcom G6 value. Using this model, we were able to predict BG in the test set with a Mean Absolute Relative Difference (MARD) of 12.7% in the normoglycemic range and 14.0% in the hyperglycemic range. Overall, 70.7% of the estimates fell within 15% of the reference value, and 79.1% fell within 20% of the reference value. While this is a relatively small participant sample, these strong initial results indicate the efficacy of this technique, and that with further refinement and more data, there is promise to achieve a clinically relevant level of accuracy. ### Competing Interest Statement Dominic Klyve has been involved as a consultant and owns stock in Know Labs. Barry Shelton and Kaptain Currie are employed by and have stock options in Know Labs. Carl Ward and David Schwarz are vendors/service providers to Know Labs. ### Funding Statement This research received no external funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of Core Human Factors, Inc. gave ethical approval for this work: 1082098495 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data are produced in the present study are not available due to privacy and ethical concerns.