From Multimodal Observation to Interpretable Suggestions: Counterfactual Time-Expanded Relational Modeling of Surgical Teams

• URL: http://arxiv.org/abs/2608.23254v1
• Date: Mon, 24 Aug 2026 13:46:46 GMT
• Title: From Multimodal Observation to Interpretable Suggestions: Counterfactual Time-Expanded Relational Modeling of Surgical Teams
• Abstract summary: We propose a tempo-relational framework for modeling surgical team dynamics from multimodal observations.<n>Such modeling enables the generation of efficient, interpretable, and actionable suggestions for clinicians.<n>This work advances surgical AI beyond outcome-driven prediction towards a socially grounded, team-centric, and actionable paradigm.
• Score: 9.813151093947097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In surgery, patient safety is threatened not only by technical issues but also by poor teamwork. However, existing surgical AI-based solutions focus mainly on visual workflow and technical execution, neglecting the modeling of team interactions and missing opportunities to actively support clinicians in improving their teamwork skills. To address this gap, we propose a tempo-relational framework for modeling surgical team dynamics from multimodal observations. By leveraging Time-Expanded graphs, the approach captures both relational structure and temporal evolution, achieving strong expressivity while remaining robust in the low-data regime typical of surgical settings. Beyond prediction, such modeling enables the generation of efficient, interpretable, and actionable suggestions for clinicians. More specifically, we generate suggestions via a counterfactual procedure that identifies minimal yet structured changes in individual behaviors and interaction patterns associated with improvements in team performance. Experiments with simulated surgical procedures show that our approach improves predictive performance in diverse behavioral and interaction goals while offering meaningful insights into team dynamics. This work advances surgical AI beyond outcome-driven prediction towards a socially grounded, team-centric, and actionable paradigm to better understand and support the development of team skills in surgical settings.

Related papers

• Towards Actionable Surgical Team Dynamics: from Teamwork to Counterfactual Annotations [8.839435095280962]
  We introduce an extended multimodal dataset for surgical team interaction analysis, built from real operating room recordings.<n>Team performance is assessed using a standardized surgical evaluation protocol, while interaction quality and individual attributes are annotated.<n>To further support the study of coordination breakdowns and performance variability, we introduce counterfactual annotations.<n>The dataset is designed to support the study of how individual actions, interaction patterns, and team-level processes jointly contribute to team outcomes in surgical settings.
  arXiv  Detail & Related papers  (2026-08-24T14:55:18Z)
• Optimize Surgical Triplet Recognition: A Knowledge-Driven Mixture-of-Experts Solution [52.149337961205624]
  We propose a Mixture-of-Experts-guided Co-Optimization framework powered by knowledge-driven learning.<n>We develop a coordinated gradient learning strategy to handle category-level conflicts.<n> Notably, inspired by surgical domain expertise, we introduce a knowledge-driven mixture-of-experts mechanism.
  arXiv  Detail & Related papers  (2026-08-24T08:32:53Z)
• Actionable Real-Time Modeling of Surgical Team Dynamics via Time-Expanded Interaction Graphs [9.813151093947097]
  We propose a real-time actionable approach for modeling surgical team dynamics using time-expanded interaction graphs.<n>This work advances surgical AI toward real-time, team-aware, and actionable decision support in the operating room.
  arXiv  Detail & Related papers  (2026-05-05T18:05:24Z)
• MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning [53.37068897861388]
  MedSAM-Agent is a framework that reformulates interactive segmentation as a multi-step autonomous decision-making process.<n>We develop a two-stage training pipeline that integrates multi-turn, end-to-end outcome verification.<n>Experiments across 6 medical modalities and 21 datasets demonstrate that MedSAM-Agent achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-02-03T09:47:49Z)
• Beyond Rigid AI: Towards Natural Human-Machine Symbiosis for Interoperative Surgical Assistance [6.832434059337678]
  This work introduces a novel Perception Agent that enables a more natural human-machine interaction in real-time surgical assistance.<n>Our agent offers the flexibility to segment both known and unseen elements in the surgical scene through intuitive interaction.
  arXiv  Detail & Related papers  (2025-07-30T20:42:24Z)
• Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance [50.486523249499115]
  Real-time video understanding is critical to guide procedures in minimally invasive surgery (MIS)<n>We propose Compress-to-Explore (C2E), a novel self-supervised framework to learn compact, informative representations from surgical videos.<n>C2E uses entropy-maximizing decoders to compress images while preserving clinically relevant details, improving encoder performance without labeled data.
  arXiv  Detail & Related papers  (2025-05-16T14:02:24Z)
• Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room [36.67627283629717]
  We present a staff-centric modeling approach that characterizes individual team members through their distinctive movement patterns and physical characteristics.<n>We develop a generalizable re-identification framework that encodes sequences of 3D point clouds to capture shape and articulated motion patterns unique to each individual.<n>Our method achieves 86.19% accuracy on realistic clinical data while maintaining 75.27% accuracy when transferring between different environments.
  arXiv  Detail & Related papers  (2025-03-17T10:30:26Z)
• SurgRAW: Multi-Agent Workflow with Chain-of-Thought Reasoning for Surgical Intelligence [16.584722724845182]
  Integration of Vision-Language Models in surgical intelligence is hindered by hallucinations, domain knowledge gaps, and limited understanding of task interdependencies.<n>We present SurgRAW, a CoT-driven multi-agent framework that delivers transparent, interpretable insights for most tasks in robotic-assisted surgery.
  arXiv  Detail & Related papers  (2025-03-13T11:23:13Z)
• Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness Assessment [66.6041949490137]
  We propose a method that integrates information from transcribed verbal feedback and corresponding surgical video to predict feedback effectiveness. Our findings show that both transcribed feedback and surgical video are individually predictive of trainee behavior changes. Our results demonstrate the potential of multi-modal learning to advance the automated assessment of surgical feedback.
  arXiv  Detail & Related papers  (2024-11-17T00:13:00Z)
• Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation [51.222684687924215]
  Surgical video-language pretraining faces unique challenges due to the knowledge domain gap and the scarcity of multi-modal data.<n>We propose a hierarchical knowledge augmentation approach and a novel Procedure-Encoded Surgical Knowledge-Augmented Video-Language Pretraining framework to tackle these issues.
  arXiv  Detail & Related papers  (2024-09-30T22:21:05Z)
• Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery [47.47211257890948]
  We propose a predictive neural network that is capable of understanding and predicting critical interactive aspects of surgical workflow from intra-abdominal video.<n>We verify our approach on established surgical datasets and applications, including the detection and prediction of action triplets.<n>Our results demonstrate the superiority of our approach compared to unstructured alternatives.
  arXiv  Detail & Related papers  (2024-02-03T00:58:05Z)
• Multimodal Semantic Scene Graphs for Holistic Modeling of Surgical Procedures [70.69948035469467]
  We take advantage of the latest computer vision methodologies for generating 3D graphs from camera views. We then introduce the Multimodal Semantic Graph Scene (MSSG) which aims at providing unified symbolic and semantic representation of surgical procedures.
  arXiv  Detail & Related papers  (2021-06-09T14:35:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.