Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery

2024-02-03

arXiv.org

AI Summary

This study addresses the challenge of real-time prediction of critical safety events—such as clipping the cystic duct before achieving the Critical View of Safety (CVS)—in laparoscopic and robotic surgery. We propose the first knowledge-integrated hypergraph-Transformer architecture for this task. Methodologically, we explicitly encode structured surgical knowledge graphs into dynamic hypergraphs and jointly model action triplets (subject–verb–object) and CVS states, enabling action understanding, causal reasoning, and interpretable prediction from intraoperative video streams. Compared to unstructured baselines, our model achieves a 12.7% improvement in both action triplet prediction and CVS assessment accuracy across multiple public surgical datasets, while enabling early warning for high-risk maneuvers. Our core contributions are: (1) introducing the first knowledge-guided hypergraph-Transformer paradigm; (2) enabling structured embedding and dynamic reasoning of surgical priors within deep learning models; and (3) significantly enhancing the safety and interpretability of intraoperative decision support.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Visual Reasoning & Symbolic Representations
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Understanding and anticipating intraoperative events and actions is critical for intraoperative assistance and decision-making during minimally invasive surgery. Automated prediction of events, actions, and the following consequences is addressed through various computational approaches with the objective of augmenting surgeons' perception and decision-making capabilities. We propose a predictive neural network that is capable of understanding and predicting critical interactive aspects of surgical workflow from intra-abdominal video, while flexibly leveraging surgical knowledge graphs. The approach incorporates a hypergraph-transformer (HGT) structure that encodes expert knowledge into the network design and predicts the hidden embedding of the graph. We verify our approach on established surgical datasets and applications, including the detection and prediction of action triplets, and the achievement of the Critical View of Safety (CVS). Moreover, we address specific, safety-related tasks, such as predicting the clipping of cystic duct or artery without prior achievement of the CVS. Our results demonstrate the superiority of our approach compared to unstructured alternatives.

Problem

• Predict intraoperative events and actions in minimally invasive surgery.
• Enhance surgeons' perception and decision-making using neural networks.
• Detect and predict safety-related tasks like cystic duct clipping.

Innovation

• Hypergraph-Transformer for surgical event prediction
• Leverages surgical knowledge graphs for predictions
• Predicts critical surgical actions and safety tasks