Harnessing Deep Learning for Early Detection of Incisional Surgical Site Infections Through Wound Image Analysis

Highlight

• Development of a convolutional neural network (CNN) to detect surgical site infections (SSIs) from wound photographs with high accuracy.
• Model training on a large multicenter, multispecialty retrospective wound image dataset (4978 images), with rigorous external validation (407 images from 95 patients) from a large academic center.
• Demonstrated model performance with an area under the curve (AUC) of 0.91 internally and 0.82 externally, showing strong discrimination and clinical utility.
• Potential for integration into telemedicine workflows to support automated triage and early diagnosis, reducing delays and variability in postoperative SSI detection.

Study Background