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Artificial Intelligence for Workflow Analysis in Colorectal Surgery: A Multicentric, Cross-Procedural Development and Generalization Study

Research Medical/Healthcare AI

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Representative image for Artificial Intelligence for Workflow Analysis in Colorectal Surgery: A Multicentric, Cross-Procedural Development and Generalization Study

Merged summary

TL;DR - AI-ColoWorkflow automates phase and step recognition in minimally invasive colorectal surgery videos using a vision transformer and temporal convolutional network. Training on pooled multicenter, multi-procedure data generally improved phase-recognition generalization, though procedure-specific models remained stronger for some step-level tasks.

  • The model combines fine-tuned DINOv3 frame features with a hierarchical multi-stage temporal convolutional network jointly trained for phase and step recognition.
  • On held-out data, it achieved 73.01% macro F1 for phase recognition and 39.82% for step recognition.
  • The pooled global model usually outperformed center- and procedure-specific alternatives, with the main exception being procedure-specific step recognition.
  • Generalization phase-recognition F1 averaged 48.42%, supporting hybrid training strategies for finer-grained surgical workflow analysis.

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Artificial Intelligence for Workflow Analysis in Colorectal Surgery: A Multicentric, Cross-Procedural Development and Generalization Study

arXiv cs.CV Pietro Mascagni, Julia Alekseenko, Pooja P Jain, Marta Goglia, Andrea Balla, Ludovica Baldari, Gianfranco Silecchia, Claudio Fiorillo, Vincenzo Tondolo, Salvador Morales-Conde, Luigi Boni, Sergio Alfieri, Nicolas Padoy 2026-08-20 arXiv:2608.20154
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-27 14:19:51.811626 UTC

TL;DR - AI-ColoWorkflow automates phase and step recognition in minimally invasive colorectal surgery videos using a vision transformer and temporal convolutional network. Training on pooled multicenter, multi-procedure data generally improved phase-recognition generalization, though procedure-specific models remained stronger for some step-level tasks.

  • The model combines fine-tuned DINOv3 frame features with a hierarchical multi-stage temporal convolutional network jointly trained for phase and step recognition.
  • On held-out data, it achieved 73.01% macro F1 for phase recognition and 39.82% for step recognition.
  • The pooled global model usually outperformed center- and procedure-specific alternatives, with the main exception being procedure-specific step recognition.
  • Generalization phase-recognition F1 averaged 48.42%, supporting hybrid training strategies for finer-grained surgical workflow analysis.
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