Mayo Clinic Advances SSI Detection with New Postoperative AI Technology

The new Vision Transformer AI model detects surgical site infections from patient-submitted photos with 73% accuracy and also reduces clinician burdens following surgeries.

Mayo Clinic has developed an artificial intelligence tool that accurately detects surgical site infections (SSIs) from patient-submitted photos in electronic health records.

Published in the Annals of Surgery, the study introduces an Vision Transformer AI model, created by the researchers to automatically identify surgical incisions, assess image quality and flag signs of infection in photos submitted by patients through online portals

The system was trained on over 20,000 images from more than 6,000 patients across nine Mayo Clinic hospitals and was detailed in a recent study published in Annals of Surgery.

Vision Transformer AI Model

The new Vision Transformer AI model detects surgical site infections from patient-submitted photos with 73% accuracy and also reduces clinician burdens following surgeries.

The model, Vision Transformer, achieved a 94% accuracy in detecting incisions and an 81% area under the curve (AUC) in identifying infections.

Cornelius Thiels, a hepatobiliary and pancreatic surgical oncologist at Mayo Clinic and co-senior author of the study, said in the statement, “Our AI model can help triage these images automatically, improving early detection and streamlining communication between patients and their care teams.”

The AI system uses a two-stage model. First, it detects whether an image contains a surgical incision and then evaluates whether that incision shows signs of infection.

This AI tool also paves the way for developing algorithms capable of detecting subtle signs of infection, potentially before they become visually apparent to the care team. This would allow for earlier treatment, decreased morbidity and reduced costs.

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