Article intro - taxonomy for surgical gestures

Surgical Endoscopy published the article "Standardization of surgical gesture taxonomy: a SAGES Delphi
consensus study" authored by Maria Clara Morais, Aditya Amit Godbole, Emaad Iqbal, Mattia Ballo, Anthony Jarc, Beatrice Van Amsterdam, Brent Matthews, Christopher M. Schlachta, Daniel A. Donoho,
Daniel A. Hashimoto, Jay A. Redan, Jayson Marwaha, Jon Gould, Liane S. Feldman, Ozanan Meireles,
Pieter De Backer, Pietro Mascagni, Simon LaPlante, Ankit Sarin, Anastasiya Shchatsko, Danielle Walsh, Danyal M. Fer, David Romero Funes, Kimimasa Sasaki, Nova Szoka, Sara S. Lazzaretti, Sharona B. Ross, Thomas Schnelldorfer, Axel Krieger, Andrew J. Hung and Filippo Filicori.

Abstract

Introduction Artificial intelligence (AI) for surgical workflow analysis often fails to generalize because surgical actions lack a standardized, fine-grained representation. Gesture-level “tokenization” of surgery, capturing instrument–tissue interac- tions as the smallest intentional functional units, offers greater technical specificity than phase- or step-level labels and has demonstrated associations with proficiency and clinical outcomes. However, the field remains fragmented by heterogeneous gesture terminology, limiting dataset interoperability and model reproducibility. Methods We conducted a SAGES-led, accelerated Delphi consensus process to establish a standardized surgical gesture taxonomy. Starting with 270 literature-derived gesture terms, we employed a novel hybrid pipeline combining large language model (LLM)-assisted semantic clustering with multi-round expert review. The process involved two Delphi surveys (open- ended, then structured agreement) with a predefined ≥ 80% agreement threshold, a pilot interactive video-based validation task where participants labeled 30 surgical clips, and a final in-person consensus meeting with live anonymous polling. Results Across iterative refinement, the taxonomy evolved from 106 gestures in 11 clusters to a hierarchical framework of Clusters, Gestures, and Sub-gestures, which, after consolidation and pilot annotation, reached a final consensus taxonomy comprising 10 clusters, 24 gestures, and 46 sub-gestures. The panel rejected dominant-instrument-only labeling, supporting multi-instrument annotation to capture assisting actions critical to surgical quality. Video-based validation demonstrated high agreement for multiple gestures (e.g., coagulate, suction, irrigate, staple, clip, needle drive), while identifying predict- able ambiguities among semantically proximate actions (e.g., cut vs seal; grasp vs clamp; dissect vs spread), informing final revisions. Conclusion This work establishes a standardized, hierarchical taxonomy for surgical gestures, providing a foundational language for surgical data science. This framework is designed to reduce annotation variability, enable reliable cross-study comparisons, and accelerate the development of scalable video-based assessment, computer vision, and autonomous systems. Defining temporal boundaries for these gestures was identified as the next critical step.

Keywords: Surgical gestures · Minimally invasive surgery · Delphi consensus 

 Source: LinkedIn