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Related Concept Videos

Anatomical Positions01:11

Anatomical Positions

In anatomy, several standard anatomical positions are used as references for describing the position and orientation of different body parts. These positions help provide a common frame of reference when discussing anatomical structures. The anatomical position is the standard reference point for describing the body's position and orientation. In this position:
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Related Experiment Video

Updated: Jul 10, 2026

Microsurgical Clip Obliteration of Middle Cerebral Aneurysm Using Intraoperative Flow Assessment
18:50

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Published on: September 25, 2009

Where are they looking in the operating room?

Keqi Chen1, Séraphin Baributsa2, Lilien Schewski3,4

  • 1CNRS, INSERM, ICube, UMR7357, University of Strasbourg, 67000, Strasbourg, France. keqi.chen@unistra.fr.

International Journal of Computer Assisted Radiology and Surgery
|April 6, 2026
PubMed
Summary

This study introduces gaze-following analysis to the operating room (OR) for improved surgical workflow understanding. The novel approach enhances clinical role prediction, surgical phase recognition, and team communication detection.

Keywords:
Clinical role predictionGaze analysisOperating roomSurgical phase recognitionTeam communication detection

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Area of Science:

  • Computer Vision
  • Surgical Data Science
  • Human-Computer Interaction

Background:

  • Gaze-following is crucial for understanding visual attention in various domains.
  • Its application in the operating room (OR) remains unexplored despite its high-stakes nature and reliance on visual cues.
  • Understanding visual attention in the OR is key for surgical workflow analysis.

Purpose of the Study:

  • Introduce gaze-following to the surgical domain for enhanced OR analysis.
  • Utilize gaze-following for understanding clinical roles, surgical phases, and team communications.
  • Demonstrate the potential of gaze-following in advancing surgical workflow analysis.

Main Methods:

  • Extended the 4D-OR and Team-OR datasets with gaze-following annotations.
  • Developed novel gaze heatmap-based and spatial-temporal models for OR tasks.
  • Employed self-supervised learning for gaze-based clip feature encoding.

Main Results:

  • Achieved state-of-the-art performance on clinical role prediction (F1=0.92) and surgical phase recognition (F1=0.95).
  • Significantly improved team communication detection by over 30% compared to existing baselines.
  • Validated the effectiveness of gaze-following models on the 4D-OR and Team-OR datasets.

Conclusions:

  • Gaze-following presents a novel and valuable research direction in surgical data science.
  • Gaze analysis from ceiling-mounted cameras offers significant clinical value for interventions.
  • Future research will focus on multi-view learning and few-shot approaches for improved robustness.