Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions

By Y. Kassahun, Bingbin Yu, A. Tibebu, Danail Stoyanov, S. Giannarou, Jan Hendrik Metzen, E. V. Poorten. 2016

215 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Surgery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Sebastian Thrun
2. Valerio Persico
3. Antonio Pescapè
4. Joseph Dean
5. Giuseppe Aceto

In Surgery

1. Volodymyr Kuleshov

Citations in the corpus

• On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, neutral): “Recent research shows an increasing interest in use of machi ne learning algorithms for modeling surgical skills, workflow , and environment and integration of this knowledge into control and automation of surgical robots [13].”
• eDoctor: machine learning and the future of medicine (2018, background): “Kassahun et al. [43] provide a succinct overview of ongoing research and advances in surgical robotics including autonomous endoscopic guidance or autonomous knot tying, executing simple tasks with performance exceeding human capabilities, as well as discovering new operating techniques that are…”
• A guide to deep learning in healthcare (2019, background): “RL can be useful in healthcare whenever learning requires physician demonstration, for instance in learning to suture wounds for robotic-assisted surgery 7 .”
• A New Tensioning Method using Deep Reinforcement Learning for Surgical Pattern Cutting (2019, background): “Machine learning in general or reinforcement learning (RL) [22, 23] in particular has been involved in a number of studies for automation of surgical tasks [24, 25].”
• The role of Information and Communication Technologies in healthcare: taxonomies, perspectives, and challenges (2018, methodology): “add more advanced functionality beyond the dexterity augmentation currently provided [157].”
• Extraction of mechanical properties of materials through deep learning from instrumented indentation (2020, unknown): “In addition to well-known applications of machine-learning (ML) algorithms in such fields as image/video analysis (26, 27) and natural language processing (28), ML has also been used for various engineering problems, such as in the discovery of new materials (29) and in health care (30).”