Wound infections represent a critical and escalating challenge to healthcare systems worldwide, contributing to patient suffering, prolonged hospitalization, amputations, and significant mortality. The associated costs are staggering: in Europe alone, >500,000 surgical site infections occur each year, with an estimated direct economic burden of €19 billion. Chronic non-healing wounds consume roughly 4 % of total healthcare expenditures in the Western world, and in a typical hospital, 25–40 % of all beds are occupied by wound patients. In the Swedish context, hospital-acquired infections affect ~60,000 patients annually, 20 % of which are surgical wound infections, adding ~107,000 SEK in cost per patient.
The emergence of multidrug-resistant (MDR) pathogens further complicates treatment, increasing the risk of chronic infections, amputations, and death. Common wound pathogens such as S. aureus, P. aeruginosa, and A. baumannii frequently exhibit resistance to multiple antibiotics.[3] Infections caused by MRSA, VRE, and carbapenem-resistant Gram-negative bacteria often require prolonged and expensive treatments and are associated with higher morbidity and mortality.
A key bottleneck in wound care is the lack of rapid, reliable diagnostics. Clinicians largely rely on visual assessment of redness, swelling, and discharge, which is subjective and often fails to identify early or polymicrobial infections. Confirmation by culture takes 36–48 h or longer, delaying intervention and forcing empiric broad-spectrum antibiotic use — fueling antimicrobial resistance (AMR). Molecular tests and sequencing methods offer higher sensitivity but are costly, slow, and rarely available at the point of care. There is a pressing need for fast, accurate, non-invasive diagnostic technologies to guide clinical decision-making, prevent overuse of antibiotics, and improve patient outcomes.
This project aims to develop a disruptive wound diagnostic platform that combines surface-enhanced Raman spectroscopy (SERS) with AI-driven spectral classification for real-time detection of wound infection, bacterial species identification, and MDR status. The project has the potential to fundamentally transform wound care by providing clinicians with actionable results within minutes, supporting targeted interventions, reducing unnecessary dressing changes, minimizing antibiotic overuse, and ultimately improving healing rates.
