Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare

Shared mental models are critical to team success; however, in practice, team members may have misaligned models due to a variety of factors. In safety-critical domains (e.g., aviation, healthcare), lack of shared mental models can lead to preventable errors and harm. Towards the goal of mitigating such preventable errors, here, we present a Bayesian approach to infer misalignment in team members' mental models during complex healthcare task execution. As an exemplary application, we demonstrate our approach using two simulated team-based scenarios, derived from actual teamwork in cardiac surgery. In these simulated experiments, our approach inferred model misalignment with over 75% recall, thereby providing a building block for enabling computer-assisted interventions to augment human cognition in the operating room and improve teamwork.

Temporally Guided Articulated Hand Pose Tracking in Surgical Videos

Articulated hand pose tracking is an underexplored problem that carries the potential for use in an extensive number of applications, especially in the medical domain. With a robust and accurate tracking system on in-vivo surgical videos, the motion dynamics and movement patterns of the hands can be captured and analyzed for rich tasks including skills assessment, training surgical residents, and temporal action recognition. In this work, we propose a novel hand pose estimation model, Res152- CondPose, which improves tracking accuracy by incorporating a hand pose prior into its pose prediction. We show improvements over state-of-the-art methods which provide frame-wise independent predictions, by following a temporally guided approach that effectively leverages past predictions. Additionally, we collect the first dataset, Surgical Hands, that provides multi-instance articulated hand pose annotations for in-vivo videos. Our dataset contains 76 video clips from 28 publicly available surgical videos and over 8.1k annotated hand pose instances. We provide bounding boxes, articulated hand pose annotations, and tracking IDs to enable multi-instance area-based and articulated tracking. When evaluated on Surgical Hands, we show our method outperforms the state-of-the-art method using mean Average Precision (mAP), to measure pose estimation accuracy, and Multiple Object Tracking Accuracy (MOTA), to assess pose tracking performance.