In an emergency setting, making a quick diagnosis under pressure is often a matter of life or death. This is especially true when it comes to diagnosing infectious diseases. Unfortunately, diagnosing infections in an emergency department is rife with challenges. Current tests either take too long, deliver unreliable results, or both. That’s where Inflammatix comes in. They are using machine learning technology to develop a point-of-care instrument that will diagnose the type of infection, and severity of infection, in emergency care quickly and effectively. Their first main product is currently in the late stages of development and can deliver a test report in about half an hour using cold blood as a sample source.

Joining me today to shed light on this incredible initiative is Ljubomir Buturovic, Vice President of Machine Learning at Inflammatix. We hear from Ljubomir about the role that machine learning played in this technology, key challenges they’ve encountered while training models on gene expression data, how they selected the 29 clinically relevant genes based on published scientific papers, plus a whole lot more. Tune in today to learn more about the groundbreaking work being done at Inflammatix and what you can expect from them in future!

“We developed an instrument which measures this gene expression for 29 clinically relevant genes for infections.” — Ljubomir Buturovic

“It takes a long time to achieve adoption. This is basically applying AI in medicine. When you are applying AI in medicine, the whole process of development and adoption works on medicine timescales, not on AI timescales.” — Ljubomir Buturovic

“One of the key challenges in applying machine learning in clinical test design is the availability of samples for training and validation. This is in sharp contrast to other applications, like maybe movie recommendations, or shopping, where you have a lot of input data, because it's relatively easy to collect.” — Ljubomir Buturovic

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