From Multimodal Observation to Interpretable Suggestions: Counterfactual Time-Expanded Relational Modeling of Surgical Teams
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,...
| Authors | Vincenzo Marco De Luca, Antonio Longa, Giovanna Varni et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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