Non-Invasive Medical Diagnostics: Know Labs' Partnership With Edge Impulse Has Potential To Improve Healthcare Using Machine Learning

Machine learning has revolutionized the field of biomedical research, enabling faster and more accurate development of algorithms that can improve healthcare outcomes. Biomedical researchers are using machine learning tools and algorithms to analyze vast and complex health data, and quickly identify patterns and relationships that were previously difficult to discern.

Know Labs, an emerging developer of non-invasive medical diagnostic technology is readying a breakthrough for non-invasive glucose monitoring, which has the potential to positively impact the lives of millions. One of the key elements behind this tech is the ability to process large amounts of novel data generated by their Bio-RFID™ radio frequency sensor, using machine learning algorithms from Edge Impulse.

The Benefits of Medical Machine Learning

One significant way in which machine learning is improving algorithm development in the biomedical space is by developing more accurate predictions and insights. Machine learning algorithms use advanced statistical techniques to identify correlations and relationships that may not be apparent to human researchers.

Machine learning algorithms can analyze a patient's entire medical history and provide predictions about their potential health outcomes, which can help medical professionals intervene earlier to prevent diseases from progressing. Machine learning algorithms can also be used to develop more personalized treatments.

Historically, this process was time-consuming and prone to error due to the difficulty in managing large datasets. Machine learning algorithms, on the other hand, can quickly and easily process vast amounts of data and identify patterns without human intervention, resulting in decreased manual workload and reduced error. 

A Future Of Improved Health Care Through Machine Learning

As the technology and use cases of machine learning continue to grow, it is evident that it can help realize a future of improved health care by unlocking the potential of large biomedical and patient datasets.

Already, early uses of machine learning in diagnosis and treatment have shown promise to diagnose breast cancer from x-rays, discover new antibiotics, predict the onset of gestational diabetes from electronic health records, and identify clusters of patients that share a molecular signature of treatment response.

With reports indicating that 400,000 hospitalized patients experience some type of preventable medical error each year, machine learning can help predict and diagnose diseases at a faster rate than most medical professionals, saving approximately $20 billion annually.

Companies like Linus Health, Viz.ai, PathAI, and Regard are showing artificial intelligence (AI) and machine learning (ML)’s ability to reduce errors and save lives.

Advancements in patient care including remote physiologic monitoring and care delivery highlights the growing demand for the use of technology to enhance non-invasive means of medical diagnosis. 

One significant area this could benefit is monitoring blood glucose non-invasively — without pricking the finger for blood, important for patients to effectively manage their type 1 and 2 diabetes. While glucose biosensors have existed for over half a century, they can be classified as two groups: electrochemical sensors relying on direct interaction with an analyte and electromagnetic sensors that leverage antennas and/or resonators to detect changes in the dielectric properties of the blood. 

Using smart devices essentially involves shining light into the body using optical sensors and quantifying how the light reflects back to measure a particular metric. Already there are smartwatches, fitness trackers, and smart rings from companies like Apple Inc. AAPL, Samsung Electronics Co Ltd. (KRX: 005930) and Google (Alphabet Inc. GOOGL ) that measure heart rate, blood oxygen levels, and a host of other metrics. 

But applying this tech to measure blood glucose is much more complicated, and the data may not be accurate. Know Labs seems to be on a path to solving this challenge. 

Using Machine Learning To Enhance Bio-RFID Technology

The Seattle-based company has partnered with Edge Impulse, providers of a machine learning development toolkit, to interpret robust data from its proprietary Bio-RFID technology. The algorithm refinement process that Edge Impulse provides is a critical step towards interpreting the existing large and novel datasets, which will ultimately support large-scale clinical research.

The Bio-RFID technology is a non-invasive medical diagnostic technology that uses a novel radio frequency sensor that can safely see through the full cellular stack to accurately identify a unique molecular signature of a wide range of organic and inorganic materials, molecules, and compositions of matter.  

Microwave and Radio Frequency sensors operate over a broader frequency range, and with this comes an extremely broad dataset that requires sophisticated algorithm development. Working with Know Labs, Edge Impulse uses its machine learning tools to train a Neural Network model to interpret this data and make blood glucose level predictions using a popular CGM proxy for blood glucose. Edge Impulse provides a user-friendly approach to machine learning that allows product developers and researchers to optimize the performance of sensory data analysis. This technology is based on AutoML and TinyML to make AI more accessible, enabling quick and efficient machine learning modeling.

The partnership between Know Labs, a company committed to making a difference in people's lives by developing convenient and affordable non-invasive medical diagnostics solutions, and Edge Impulse, makers of tools that enable the creation and deployment of advanced AI algorithms, is a prime example for how responsible machine learning applications could significantly improve and change healthcare diagnostics.

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