A surgical activity model of laparoscopic cholecystectomy for co-operation with collaborative robots

Background Laparoscopic cholecystectomy is a very frequent surgical procedure. However, in an ageing society, less surgical staff will need to perform surgery on patients. Collaborative surgical robots (cobots) could address surgical staff shortages and workload. To achieve context-awareness for surgeon-robot collaboration, the intraoperative action workflow recognition is a key challenge. Methods A surgical process model was developed for intraoperative surgical activities including actor, instrument, action and target in laparoscopic cholecystectomy (excluding camera guidance). These activities, as well as instrument presence and surgical phases were annotated in videos of laparoscopic cholecystectomy performed on human patients ( n  = 10) and on explanted porcine livers ( n  = 10). The machine learning algorithm Distilled-Swin was trained on our own annotated dataset and the CholecT45 dataset. The validation of the model was conducted using a fivefold cross-validation approach. Results In total, 22,351 activities were annotated with a cumulative duration of 24.9 h of video segments. The machine learning algorithm trained and validated on our own dataset scored a mean average precision (mAP) of 25.7% and a top K  = 5 accuracy of 85.3%. With training and validation on our dataset and CholecT45, the algorithm scored a mAP of 37.9%. Conclusions An activity model was developed and applied for the fine-granular annotation of laparoscopic cholecystectomies in two surgical settings. A machine recognition algorithm trained on our own annotated dataset and CholecT45 achieved a higher performance than training only on CholecT45 and can recognize frequently occurring activities well, but not infrequent activities. The analysis of an annotated dataset allowed for the quantification of the potential of collaborative surgical robots to address the workload of surgical staff. If collaborative surgical robots could grasp and hold tissue, up to 83.5% of the assistant’s tissue interacting tasks (i.e. excluding camera guidance) could be performed by robots.Open access funding enabled and organized by Projekt DEAL.Open Access funding enabled and organized by Projekt DEAL.Bundesministerium für Bildung und Forschunghttp://dx.doi.org/10.13039/501100002347Deutsche Forschungsgemeinschafthttp://dx.doi.org/10.13039/501100001659Technische Universität Dresden (1019

Free-living amoebae and their associated bacteria in Austrian cooling towers: a 1-year routine screening

Free-living amoebae (FLA) are widely spread in the environment and known to cause rare but often serious infections. Besides this, FLA may serve as vehicles for bacterial pathogens. In particular, Legionella pneumophila is known to replicate within FLA thereby also gaining enhanced infectivity. Cooling towers have been the source of outbreaks of Legionnaires' disease in the past and are thus usually screened for legionellae on a routine basis, not considering, however, FLA and their vehicle function. The aim of this study was to incorporate a screening system for host amoebae into a Legionella routine screening. A new real-time PCR-based screening system for various groups of FLA was established. Three cooling towers were screened every 2 weeks over the period of 1 year for FLA and Legionella spp., by culture and molecular methods in parallel. Altogether, 83.3 % of the cooling tower samples were positive for FLA, Acanthamoeba being the dominating genus. Interestingly, 69.7 % of the cooling tower samples were not suitable for the standard Legionella screening due to their high organic burden. In the remaining samples, positivity for Legionella spp. was 25 % by culture, but overall positivity was 50 % by molecular methods. Several amoebal isolates revealed intracellular bacteria.TRP 209-B20(VLID)308958