Aliado - A design concept of AI for decision support in oncological liver surgery.

Background: The interest in artificial intelligence (AI) is increasing. Systematic reviews suggest that there are many machine learning algorithms in surgery, however, only a minority of the studies integrate AI applications in clinical workflows. Our objective was to design and evaluate a concept to use different kinds of AI for decision support in oncological liver surgery along the treatment path.

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.

One model to use them all: training a segmentation model with complementary datasets.

Purpose: Understanding surgical scenes is crucial for computer-assisted surgery systems to provide intelligent assistance functionality. One way of achieving this is via scene segmentation using machine learning (ML). However, such ML models require large amounts of annotated training data, containing examples of all relevant object classes, which are rarely available.

The development of tissue handling skills is sufficient and comparable after training in virtual reality or on a surgical robotic system: a prospective randomized trial.

Background: Virtual reality is a frequently chosen method for learning the basics of robotic surgery. However, it is unclear whether tissue handling is adequately trained in VR training compared to training on a real robotic system.

Methods: In this randomized controlled trial, participants were split into two groups for "Fundamentals of Robotic Surgery (FRS)" training on either a DaVinci VR simulator (VR group) or a DaVinci robotic system (Robot group).