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Related Experiment Video

Updated: Apr 13, 2026

Endoscopic Endonasal Trans-sphenoidal Approach: Minimally Invasive Surgery for Pituitary Adenomas
07:43

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Action classification for endoscopic pituitary adenoma resection: a consensus-based study.

Joachim Starup-Hansen1,2, Danyal Z Khan3,4, Adrito Das4

  • 1Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK. j.starup-hansen@ucl.ac.uk.

International Journal of Computer Assisted Radiology and Surgery
|April 11, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a detailed classification system for surgical actions during endoscopic pituitary adenoma resection. This ontology provides a foundation for AI to analyze surgical skills and predict outcomes.

Keywords:
Artificial intelligenceEndoscopic neurosurgeryOperative workflowPituitary surgerySurgical skill assessmentSurgical video annotation

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Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Endoscopic pituitary adenoma resection is a complex procedure where surgical skill significantly impacts outcomes.
  • Current workflow analyses lack the detail to identify skill-related variations in surgical technique.
  • Action-level analysis is crucial for understanding and quantifying surgical skill but is underutilized in AI-driven workflow analysis.

Purpose of the Study:

  • To develop and validate a reproducible action-level classification ontology for endoscopic pituitary adenoma resection.
  • To establish a structured annotation foundation for future AI-based workflow and skill analysis.
  • To enable objective assessment of surgical technique in pituitary adenoma surgery.

Main Methods:

  • A multi-disciplinary panel of neurosurgeons and data scientists developed a standardized classification system by reviewing endoscopic videos of pituitary adenoma resections.
  • Surgical actions were annotated using a triplet structure (instrument, target, verb) with temporal data.
  • Inter-annotator agreement was assessed using Cohen's Kappa to ensure framework reliability.

Main Results:

  • A consensus-based ontology was created, classifying actions into 9 verbs, 12 instruments, and 7 targets.
  • Action distributions varied between micro- and macroadenomas, with different primary actions observed for each.
  • The annotation system demonstrated substantial to near-perfect inter-rater reliability (κ = 0.69–0.95).

Conclusions:

  • A robust and interpretable action classification ontology for pituitary adenoma resection was established.
  • The ontology facilitates high-quality, standardized labeling for computer-vision AI applications.
  • This work lays the foundation for using action-level annotations to improve surgical outcome prediction and automated skill assessment.