SWAG: long-term surgical workflow prediction with generative-based anticipation.

ABSTRACT:

Purpose

While existing approaches excel at recognising current surgical phases, they provide limited foresight and intraoperative guidance into future procedural steps. Similarly, current anticipation methods are constrained to predicting short-term and single events, neglecting the dense, repetitive, and long sequential nature of surgical workflows. To address these needs and limitations, we propose SWAG (surgical workflow anticipative generation), a framework that combines phase recognition and anticipation using a generative approach.

Methods

This paper investigates two distinct decoding methods-single-pass (SP) and autoregressive (AR)-to generate sequences of future surgical phases at minute intervals over long horizons. We propose a novel embedding approach using class

SUBMITTER: Boels M 

PROVIDER: S-EPMC12575541 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature