AI model detects infections from wound photos

Early triage of wound photos could help doctors prioritise urgent cases, especially in rural settings.
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Mayo Clinic researchers have developed an AI system capable of detecting surgical site infections from wound photographs submitted by patients. The model was trained using over 20,000 images from more than 6,000 persons across nine hospital locations.

The AI pipeline identifies whether a photo contains a surgical incision and then evaluates that incision for infection. Known as Vision Transformer, the model accurately recognises incisions and scores high in AUC in infection detection.

Medical staff review outpatient wound images manually, which can delay care and burden resources. Automating this process may improve early diagnosis, reduce unnecessary visits, and speed up responses to high-risk cases.

Researchers believe the tool could eventually serve as a frontline screening method, especially helpful in rural or understaffed areas. Consistent performance across diverse patient groups also suggests a lower risk of algorithmic bias, though further validation remains essential.

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