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

Pituitary adenoma resection via the endoscopic transsphenoidal approach is technically demanding, with outcomes influenced by surgical skill. However, the association between technique and outcomes remains poorly defined. Existing workflow analyses focus on broad procedural steps and phases, but a more detailed, action-level approach is needed to capture skill-related variation. While AI shows promise in automating workflow analysis, its use at the action level is limited. This study develops and validates a reproducible action-level classification ontology for endoscopic pituitary adenoma resection, establishing the structured annotation foundation required for future AI-based workflow and skill analysis.

Endoscopic videos of primary pituitary adenoma resections were collected from two high-volume international pituitary centres. A multi-disciplinary panel of neurosurgeons and data scientists iteratively reviewed and annotated surgical actions to establish a standardized classification system. Actions were categorized into triplets (instrument, target, verb), with additional temporal annotations. To evaluate framework reliability, an independent annotator followed a structured annotation guide, and inter-annotator agreement was measured using Cohen's Kappa.

A consensus-based classification ontology was developed, comprising 9 verbs, 12 instruments, and 7 targets from the review of 18 endoscopic pituitary adenoma resections (9 microadenomas, 9 macroadenomas). Action distribution differed between micro- and macroadenomas, with grasping being the predominant action in microadenomas (72% of right-hand frames) and blunt dissection and traction dominating macroadenomas. The left hand primarily performed non-meaningful movements (88% of macroadenoma frames, 55% of microadenoma frames), while the right hand was responsible for more deliberate tool-tissue interactions. Inter-rater reliability analysis demonstrated substantial to near-perfect agreement (κ = 0.69-0.95), confirming the reproducibility of the annotation system.

While acknowledging that conclusions remain limited by dataset size and validation stability, this study establishes a robust and interpretable action classification ontology for pituitary adenoma resection. The ontology enables high-quality, standardized labelling for future computer-vision AI works, and lays the groundwork for evaluating whether action-level annotation improves surgical outcome prediction and automated skill assessment.