OC-004 CAN AI HELP IN EARLY PREDICTION OF SURGICAL SITE INFECTIONS?

Abstract Background The use of Artificial Intelligence (AI) in healthcare has the potential to transform medical diagnosis, including the detection of surgical site infections (SSI). Real-time monitoring of surgical sites and early detection of infection through AI can lead to prompt treatment, improving patient outcomes and reducing healthcare costs and spread of infections in healthcare facilities. Aim This study aimed to determine the use of AI in the early detection of surgical site infections and its impact on patient recovery. Materials and methods The study analysed patients admitted to the hospital over the past year with SSIs. A novel AI device was used to image the wound and detect presence of infection through fluorescence lights that identify bacteria through bioluminescence. Targeted tissue cultures were taken, antibiotics were administered. Some wounds required targeted debridement. Daily dressings were done followed by secondary suturing. Results A total of 34 patients were included in the study, with a mean age of 52.4 years and 14 female and 20 male patients. 25 patients reported pus from the wound, 9 had serous discharge, and 19 had slough present. In 85.2% of patients, the AI device's gram stain for bacteria in the wound was similar to the tissue culture report. The mean time for wound healing was 39.52 days. Conclusion Our study suggests that AI devices can accurately identify the gram stain of bacteria in a wound, leading to prompt antibiotic treatment for patients with SSIs. This could have significant benefits in improving patient outcomes and healthcare costs.