Applying plasmonic-enhanced infrared light to a liquid biopsy provides its molecular “fingerprint”. This fingerprint represents the collective response of all molecules within the sample to the vibration of their atomic bonds, allowing us to achieve a holistic analysis of the sample. We achieve a holistic analysis of the sample.

Alterations in this fingerprint are correlated with different diseases. Our machine learning models detect these alterations, identifying the associated illness with great precision. Through our machine learning development, we detect these alterations, identifying the diseases associated with them with great precision.

The use of advanced statistical classification algorithms enables multi-disease detection from a single liquid biopsy analysis. It is a rapid and precise method for identifying illness well before any symptoms manifest. It is a fast and accurate way to identify the disease long before its symptoms manifest.

Our technology overcomes the limitations of conventional methods and allows for more comprehensive and personalised reports. This means a greater chance of detecting cancer early and better patient outcomes. This means more possibilities to detect cancer in time and better outcomes for the patient.