Towards Safe Dissection: A Structured Multimodal State Representation for Laparoscopic Surgery

Abstract

Introduction: The development of intelligent support systems for minimally invasive surgery requires careful awareness of how safety is maintained in automated decision processes. Deep learning has notably contributed to the analysis of laparoscopic recordings, improving anatomical segmentation, phase detection, and gesture recognition. However, most known approaches remain limited to predictions. Although they may not fully describe how the operational situation advances over time or how actions should respond to that evolution.

Objectives: This work aims to give a structured state representation that helps safe learning in surgical conditions, where reward and imitation learning cannot depend on unrestrained exploration.

Methods: Laparoscopic surgery is modeled as a sequential and partially observable process.  A multimodal state is generated by merging anatomical context, a safety indicator derived from the Critical View of Safety, gesture dynamics, procedural phase information, and active instrument configuration into a cohesive picture.

Results: Experimental evaluation shows that the proposed structured state architecture produces a coherent representation of the dynamic surgical environment, allowing trustworthy learning from recorded expert procedures.

Conclusions: This study reveals that such structured state building provides a cohesive platform for dependable data-driven help in complicated Healthcare 5.0 scenarios.