Anomaly detection using intraoperative iKnife data: a comparative analysis in breast cancer surgery.

Journal: International journal of computer assisted radiology and surgery
Published Date: Jul 29, 2025

Abstract

PURPOSE: Intraoperative margin assessment is crucial to ensure complete tumor removal and minimize the risk of cancer recurrence during breast-conserving surgery. The Intelligent Knife (iKnife), a mass spectrometry device that analyzes surgical smoke, shows promise in near-real-time margin evaluation. However, current AI models depend on labeled ex-vivo datasets, which are costly and time-consuming to produce. This research explores the potential of machine learning anomaly detection models to reduce reliance on labeled ex-vivo datasets by utilizing unlabeled intraoperative spectra.

Authors

  • Olivia Radcliffe
    School of Computing, Queen's University, Kingston, ON, Canada. 19omr@queensu.ca.
  • Laura Connolly
    School of Computing, Queen's University, Kingston, ON, Canada.
  • Amoon Jamzad
    School of Computing, Queen's University, Kingston, ON, Canada.
  • Martin Kaufmann
    Department of Medicine, Queen's University, Kingston, ON, Canada.
  • Shaila Merchant
    Department of Surgery, Queen's University, Ontario, Canada.
  • Jay Engel
    Department of Surgery, Queen's University, Ontario, Canada.
  • Ross Walker
    Department of Surgery, Queen's University, Kingston, ON, Canada.
  • Sonal Varma
    Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada.
  • Gabor Fichtinger
    Department of Mechanical and Material Engineering, Queen's University, Kingston, ON, Canada.
  • John Rudan
    Department of Surgery, Kingston Health Sciences Centre, Kingston, ON K7L 2V7, Canada.
  • Parvin Mousavi
    Medical Informatics Laboratory, School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada.

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