# Event-Level Detection of Surgical Instrument Handovers in Videos with Interpretable Vision Models Source: [Event-Level Detection of Surgical Instrument Handovers in Videos with Interpretable Vision Models]() Collection index: [llms.txt]() Data updated: 2026-04-11T05:00:04.810Z Authors: Katerina Katsarou, George Zountsas, Karam Tomotaki-Dawoud, Alexander Ehrenhoefer, Paul Chojecki, David Przewozny, Igor Maximilian Sauer, Amira Mouakher, Sebastian Bosse Published: 2026-04-08T20:31:28.000Z ## Abstract Reliable monitoring of surgical instrument exchanges is essential for maintaining procedural efficiency and patient safety in the operating room. Automatic detection of instrument handovers in intraoperative video remains challenging due to frequent occlusions, background clutter, and the temporally evolving nature of interaction events. We propose a spatiotemporal vision framework for event-level detection and direction classification of surgical instrument handovers in surgical videos. The model combines a Vision Transformer (ViT) backbone for spatial feature extraction with a unidirectional Long Short-Term Memory (LSTM) network for temporal aggregation. A unified multi-task formulation jointly predicts handover occurrence and interaction direction, enabling consistent modeling of transfer dynamics while avoiding error propagation typical of cascaded pipelines. Predicted confidence scores form a temporal signal over the video, from which discrete handover events are identified via peak detection. Experiments on a dataset of kidney transplant procedures demonstrate strong performance, achieving an F1-score of 0.84 for handover detection and a mean F1-score of 0.72 for direction classification, outperforming both a single-task variant and a VideoMamba-based baseline for direction prediction while maintaining comparable detection performance. To improve interpretability, we employ Layer-CAM attribution to visualize spatial regions driving model decisions, highlighting hand-instrument interaction cues. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Handover Detection on Kidney Transplant Procedure Dataset (test)](): Handover Detection; Kidney Transplant Procedure Dataset (test); Precision: 97. - [Handover Direction Classification on Kidney Transplant Procedure Dataset (test)](): Handover Direction Classification; Kidney Transplant Procedure Dataset (test); F1 Score (R): 70. ## Original sources and implementation [Original paper]() [PDF]()