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Imaging-based Surgical Site Infection Detection Using Artificial Intelligence.

Hala Muaddi1, Ashok Choudhary2, Frank Lee1

  • 1Division of Hepatobiliary and Pancreas Surgery, Mayo Clinic, Rochester, MN.

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|July 3, 2025
PubMed
Summary
This summary is machine-generated.

An artificial intelligence pipeline effectively assesses patient-submitted wound images for surgical site infections (SSIs). This AI tool aids in early detection, reducing clinician workload and improving postoperative care outcomes.

Keywords:
artificial intelligencepostoperative monitoringsurgical site infection

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Area of Science:

  • Medical Artificial Intelligence
  • Digital Health
  • Surgical Site Infection Detection

Background:

  • Increasing outpatient surgeries and remote monitoring generate significant administrative tasks for clinicians.
  • Early detection of surgical site infections (SSIs) is critical for minimizing postoperative complications.
  • Patient-submitted wound images via online portals are becoming more common.

Purpose of the Study:

  • To create an artificial intelligence (AI)-based system for evaluating and prioritizing patient-submitted postoperative wound images.
  • To automate the assessment of wound images and detect potential surgical site infections (SSIs).

Main Methods:

  • Developed a two-stage AI model for incision detection and SSI detection in postoperative wound images.
  • Utilized a dataset of 6060 patient images from Mayo Clinic hospitals (2019-2022), including SSI outcomes from the National Surgical Quality Improvement Program (NSQIP).
  • Evaluated four pretrained architectures using cross-validation, data augmentation, and image quality assessment, including sensitivity analysis across racial groups.

Main Results:

  • The Vision Transformer model achieved high accuracy in incision detection (0.94) and SSI detection (0.73).
  • The AI pipeline demonstrated robust performance in image quality assessment.
  • Performance was consistent across different racial subgroups, indicating equitable effectiveness.

Conclusions:

  • The developed AI pipeline shows significant potential for automating the assessment of postoperative wound images.
  • This technology can effectively aid in the early detection of surgical site infections (SSIs).
  • The AI system promises to reduce clinician workload and enhance the quality of postoperative patient care.