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
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This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.