
Non-Invasive Diagnostic Technology Creates Unique AI Models for Individual Diseases with Dejan Nenov Luventix TRANSCRIPT
Dejan Nenov is the Founder of Luventix, an AI-powered diagnostic platform that uses machine learning to analyze metabolic data from urine samples to detect multiple diseases and health conditions. This approach, inspired by the ability of dogs to detect cancer through smell, enables a single non-invasive test to screen for conditions across oncology, neurology, immunology, and other areas at a fraction of the cost of genetic sequencing or blood tests. The platform has applications in precision medicine, patient stratification for clinical trials, and population-level health screening.
Dejan explains, "The origin story is actually quite fun. One of my two co-founders and I were sitting around his dining table after a day of skiing and watching TV, and we saw dogs smelling urine and sitting down when the urine was from a cancer patient. And so we started thinking, can we reproduce this with technology? And we looked at it and said, "Well, what does the dog's nose do?" And we said, "Well, it's identifying the molecules in the volatiles of the sample, the urine. That looks suspiciously like gas chromatography."
"And then what happens? Well, the dog's brain determines whether the sample is positive or negative. And that's a classifier, a classic classification problem based on a dataset. So we roll back a year or so, and my co-founder Martin did a study to explore whether he could build models to do what the dogs did, but consistently in a mechanistically repeatable fashion."
"We demonstrated that, yes, indeed, based on creating a digital twin of the patient's organism using metabolic data from a urine sample, we're able to train models that can determine whether a disease or condition is present from a urine sample. In the most general case, that's sort of a patient stratification technology."
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