Injured Avatars: The Impact of Embodied Anatomies and Virtual Injuries on Well-Being and Performance.

Human cognition relies on embodiment as a fundamental mechanism. Virtual avatars allow users to experience the adaptation, control, and perceptual illusion of alternative bodies. Although virtual bodies have medical applications in motor rehabilitation and therapeutic interventions, their potential for learning anatomy and medical communication remains underexplored. For learners and patients, anatomy, procedures, and medical imaging can be abstract and difficult to grasp. Experiencing anatomies, injuries, and treatments virtually through one's own body could be a valuable tool for fostering understanding. This work investigates the impact of avatars displaying anatomy and injuries suitable for such medical simulations. We ran a user study utilizing a skeleton avatar and virtual injuries, comparing to a healthy human avatar as a baseline. We evaluate the influence on embodiment, well-being, and presence with self-report questionnaires, as well as motor performance via an arm movement task. Our results show that while both anatomical representation and injuries increase feelings of eeriness, there are no negative effects on embodiment, well-being, presence, or motor performance. These findings suggest that virtual representations of anatomy and injuries are suitable for medical visualizations targeting learning or communication without significantly affecting users' mental state or physical control within the simulation.

Deep Learning in Surgical Workflow Analysis: A Review of Phase and Step Recognition.

OBJECTIVE: In the last two decades, there has been a growing interest in exploring surgical procedures with statistical models to analyze operations at different semantic levels. This information is necessary for developing context-aware intelligent systems, which can assist the physicians during operations, evaluate procedures afterward or help the management team to effectively utilize the operating room. The objective is to extract reliable patterns from surgical data for the robust estimation of surgical activities performed during operations. The purpose of this article is to review the state-of-the-art deep learning methods that have been published after 2018 for analyzing surgical workflows, with a focus on phase and step recognition. METHODS: Three databases, IEEE Xplore, Scopus, and PubMed were searched, and additional studies are added through a manual search. After the database search, 343 studies were screened and a total of 44 studies are selected for this review. CONCLUSION: The use of temporal information is essential for identifying the next surgical action. Contemporary methods used mainly RNNs, hierarchical CNNs, and Transformers to preserve long-distance temporal relations. The lack of large publicly available datasets for various procedures is a great challenge for the development of new and robust models. As supervised learning strategies are used to show proof-of-concept, self-supervised, semi-supervised, or active learning methods are used to mitigate dependency on annotated data. SIGNIFICANCE: The present study provides a comprehensive review of recent methods in surgical workflow analysis, summarizes commonly used architectures, datasets, and discusses challenges.