title: Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions.
abstract: OBJECTIVE
  Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room.
  
  
  METHODS
  The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive.
  
  
  RESULTS
  Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices.
  
  
  CONCLUSIONS
  ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical robotics. Current devices possess no intelligence whatsoever and are merely advanced and expensive instruments.
authors.affiliation: Robotics Innovation Center, German Research Center for Artificial Intelligence, Robert-Hooke-Str. 1, 28359, Bremen, Germany. yohannes.kassahun@dfki.de.
authors.forename: Yohannes
authors.initials: Y
authors.lastname: Kassahun
authors.affiliation: Faculty 3 - Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 1, 28359, Bremen, Germany.
authors.forename: Bingbin
authors.initials: B
authors.lastname: Yu
authors.affiliation: Faculty 3 - Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 1, 28359, Bremen, Germany.
authors.forename: Abraham Temesgen
authors.initials: AT
authors.lastname: Tibebu
authors.affiliation: Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
authors.forename: Danail
authors.initials: D
authors.lastname: Stoyanov
authors.affiliation: Hamlyn Centre for Robotic Surgery, Imperial College London, London, UK.
authors.forename: Stamatia
authors.initials: S
authors.lastname: Giannarou
authors.affiliation: Faculty 3 - Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 1, 28359, Bremen, Germany.
authors.forename: Jan Hendrik
authors.initials: JH
authors.lastname: Metzen
authors.affiliation: Department of Mechanical Engineering, University of Leuven, Celestijnenlaan 300B, 3001, Heverlee, Belgium.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Vander Poorten
mesh_terms: D004867:Equipment Design; D006801:Humans; D000069550:Machine Learning; D012371:Robotics / Q000295:instrumentation*
keywords: Learning to perceive; Skill analysis; Skill learning; Surgical robotics