CCI Photonics – Lets save a life in 15 minutes: Rapid detection of antimicrobial resistant infectious diseases

Antimicrobial resistance (AMR) is a major public health threat that requires the development of new and innovative methods for early and rapid identification of AMR microorganisms. Current methods for identifying AMR microorganisms are often time-consuming, taking up to 48 hours to yield a result, and produce large amounts of laboratory waste during their analysis. In addition, delayed identification of AMR microorganisms leads to inappropriate treatment leading to an increase in morbidity and mortality rates and excessive prescription of broad spectrum antibiotics which further accelerates the growth of AMR

In this project, we have developed a rapid method for dentification of AMR microorganisms and their antibiotic sensitivity profiling that can use either a direct biofluid sample from the patient or a cultured microorganism. Our approach involves the use of infrared spectroscopy analysed using ML to obtain results, which allows for accurate, with a sensitivity and specificity above 97%, and efficient identification of AMR microorganisms. This technology is highly scalable and can be easily integrated into existing laboratory workflows, allowing clinicians to quickly and accurately identify AMR microorganisms and provide patients with the appropriate antibiotic treatment, ultimately leading to an increase in patients' quality of life.

Another key innovation of our approach is that the same method can use either biofluids such as saliva or urine directly or bacteria cultured using routine laboratory methods. This means that our method can be used in a variety of clinical settings either in a diagnostic microbiology lab or as a near patient test.

In addition the only consumable used in sample processing is a glass slide which will significantly reduce laboratory waste.

Overall, our project has demonstrated a rapid method for identification of AMR microorganisms that can use a direct patient sample or a cultured microorganism to be processed using spectroscopy and analysed with ML to obtain results. Our approach has the potential to improve patient outcomes, by ensuring that appropriate antibiotics can be used early in the course of infectious disease and reduce the spread of drug-resistant infections. We believe that this approach represents a significant step forward in the fight against antimicrobial resistance and has the potential to benefit patients worldwide.