TitleRole of Smart Image Segmentation in Infection Diagnosis and Monitoring of Acute Surgical Wounds

Background/Purpose: There are challenges in applying artificial intelligence (AI) for acute wound healing assessment due to limited data availability, the dynamic nature of healing, and inter-patient variability. These challenges require further research on data collection, algorithm design, and validation techniques specific to each wound type. This study proposes a computer vision model for identifying acute surgical wounds using image segmentation techniques. Methods: The ACW.ai model was used to segment wound images and assign various labels to different wound features, mimicking a clinician’s perspective. The model was trained and validated using ACW.ai on a chronic wound dataset (FUSeg). Results: The ACW.ai model achieved a DICE score of 90.9%, outperforming existing methods. Additionally, the model was trained on a private dataset of 59 acute wound images, achieving a DICE score of 87.90% and a mean average precision of 82.10%. Conclusion: Image segmentation is crucial for the early detection of surgical site infections (SSIs) and facilitates better assessment of wound healing. Healthcare professionals may identify potential signs of infection and monitor healing progress by segmenting wounds and analyzing specific features, enabling them to take preventive actions and improve patient outcomes.