Surgical endoscopy · 2026
Standardization of surgical gesture taxonomy: a SAGES Delphi consensus study.
СтатьяНазвание и аннотация: Перевод в очереди
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Morais Maria Clara, Godbole Aditya Amit, Iqbal Emaad, Ballo Mattia, Jarc Anthony, Van Amsterdam Beatrice, Matthews Brent, Schlachta Christopher M, Donoho Daniel A, Hashimoto Daniel A, Redan Jay A, Marwaha Jayson, Gould Jon, Feldman Liane S, Meireles Ozanan, De Backer Pieter, Mascagni Pietro, LaPlante Simon, Sarin Ankit, Shchatsko Anastasiya, Walsh Danielle, Fer Danyal M, Funes David Romero, Sasaki Kimimasa, Szoka Nova, Lazzaretti Sara S, Ross Sharona B, Schnelldorfer Thomas, Krieger Axel, Hung Andrew J, Filicori Filippo
Аннотация
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.
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.