Collision feedback about instrument and environment interaction is often lacking in robotic surgery training devices. The PoLaRS virtual reality simulator is a newly developed desk trainer that overcomes drawbacks of existing robot trainers for advanced laparoscopy. This study aimed to assess the effect of haptic and visual feedback during training on the performance of a robotic surgical task. Robotic surgery-naïve participants were randomized and equally divided into two training groups: Haptic and Visual Feedback (HVF) and No Haptic and Visual Feedback. Participants performed two basic virtual reality training tasks on the PoLaRS system as a pre- and post-test. The measurement parameters Time, Tip-to-tip distance, Path length Left/Right and Collisions Left/Right were used to analyze the learning curves and statistically compare the pre- and post-tests performances. In total, 198 trials performed by 22 participants were included. The visual and haptic feedback did not negatively influence the time to complete the tasks. Although no improvement in skill was observed between pre- and post-tests, the mean rank of the number of collisions of the right grasper (dominant hand) was significantly lower in the HVF feedback group during the second post-test (Mean Rank = 8.73 versus Mean Rank = 14.27, U = 30.00, p = 0.045). Haptic and visual feedback during the training on the PoLaRS system resulted in fewer instrument collisions. These results warrant the introduction of haptic feedback in subjects with no experience in robotic surgery. The PoLaRS system can be utilized to remotely optimize instrument handling before commencing robotic surgery in the operating room.

Background: Surgical instrument counting is a manual, attention-intensive task of the operating room (OR) nurse. Many labour-saving technologies have been proposed, but implementation remains challenging. Knowledge of current counting methods and staff preferences could guide future developments towards effective application. Approach: We observed OR nurses counting materials and instruments in 50 surgical procedures performed by various surgical specialties in a regional teaching hospital in Delft, The Netherlands. Additionally, we surveyed them on their preferences concerning the methods of counting. Key findings: Variations in approaches of surgical counting were observed, with OR nurses using multiple strategies and counting techniques to manage disruptions and limit workload. Interest in using supportive technology is limited to the preoperative and postoperative phase. Relevance: This research relates observational data to staff preferences. Our findings may guide future developments of labour-saving innovations regarding surgical counting towards developing more effective applications and to ensure successful implementation.

Deep learning-based object detectors, while offering exceptional performance, are data-dependent and can suffer from generalization issues. In this work, we investigated deep neural networks for detecting people and medical instruments for the vision-based workflow analysis system inside Catheterization Laboratories (Cath Labs). The central problem explored in this paper is the fact that the performance of the detector can degrade drastically if it is trained and tested on data from different Cath Labs. Our research aimed to investigate the underlying causes of this specific performance degradation and find solutions to mitigate this issue. We employed the YOLOv8 object detector and created datasets from clinical procedures recorded at Reinier de Graaf Hospital (RdGG) and Philips Best Campus, supplemented with publicly accessible images. Through a series of experiments complemented by data visualization, we discovered that the performance degradation primarily stems from data distribution shifts in the feature space. Notably, the object detector trained on non-sensitive online images can generalize to unseen Cath Labs, outperforming the model trained on a procedure recording from a different Cath Lab. The detector trained on the online images achieved an mAP@0.5 of 0.517 on the RdGG dataset. Furthermore, by switching to the most suitable camera for each object in the Cath Lab, the multi-camera system can further improve the detection performance significantly. An aggregated L-camera mAP@0.5 of 0.679 is achieved for single-object classes on the RdGG dataset.

When making a decision on the operating room air handling installation and type of air supply system, it is relevant to know the expenditures of the different air handling installations and air supply systems. The aim of this study was to determine the capital and operational expenditures of air handling installations equipped with an ultra-clean or with a conventional system. To compare the technical requirements of Dutch air handling installations with European standards and guidelines, and evaluate the costs of surgical site infections in comparison with the capital expenditures. This study fills a gap in knowledge, detailed technical information and costs of air handling installations and air supply systems from multiple completed projects of 24 hospitals were collected, analyzed and compared. Per OR capital expenditures increase by €62,491 to €139,018 when an air handling installation with an ultra-clean system is compared to a conventional system, which is 3%–7% of the total construction costs of a completely new OR department. The yearly increase in operational expenditures per OR with an ultra-clean system compared to a conventional system was €673 to €1,896. The capital and operational expenditures of air handling installations with an ultra-clean system are higher than those with a conventional system. The technical specifications of the ORs studied in the Netherlands correspond to European standards and guidelines. When the impact on patient suffering and costs associated with surgical site infections are weighed against the investment required for an air handling installation with an ultra-clean system, it is worth considering.

The authors regret that they have missed during the review process that the legend belonging to figure 6a, 6b, 6c and 6d is not shown. It is necessary to show the legenda otherwise the readers cannot interpret the graphs. Apologies that we missed it during the review process of the article. The legend belonging to figure 6 is:[Formula presented] The authors would like to apologize for any inconvenience caused.