Scholarlink

Introduction <p>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 interactions 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.</p> Methods <p>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 ≥&#xa0;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.</p> Results <p>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 predictable ambiguities among semantically proximate actions (e.g., cut vs seal; grasp vs clamp; dissect vs spread), informing final revisions.</p> Conclusion <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Standardization of surgical gesture taxonomy: a SAGES Delphi consensus study

  • 期刊论文

  • 发表日期：

    2026年5月15日

查看全文
  • 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,
  • Filippo Filicori

摘要

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 interactions 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 predictable 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.