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Cited by 238Surgical Simulation and TrainingSoft Robotics and ApplicationsRobot Manipulation and Learning

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

Kassahun, Yohannes, Yu, Bingbin, Tibebu, Abraham Temesgen, Stoyanov, Danail, Giannarou, Stamatia, Metzen, Jan Hendrik, Vander Poorten, Emmanuel·2016·International Journal of Computer Assisted Radiology and Surgery
DOI 10.1007/s11548-015-1305-z
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Abstract

Purpose 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. Conclusion 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.

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  • The main challenges in applying machine learning in surgery and robotic surgery are a lack of high-quality medical and surgical data, a lack of reliable metrics that adequately reflect learning characteristics, and a lack of a structured approach to effective transfer of surgical skills for automated execution.

    “The main issues are a lack of high-quality medical and surgical data, a lack of reliable metrics that adequately reflect learning characteristics, and a lack of a structured approach to the effective transfer of surgical skills for automated execution [64].”

    From Advanced Applications of Industrial Robotics: New Trends and Possibilities (2021) · cites this paper as [64]

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Authors
Kassahun, Y., Yu, B., Tibebu, A. T., Stoyanov, D., Giannarou, S., Metzen, J. H., & Vander Poorten, E.
Affiliations
  • German Research Centre for Artificial Intelligence (DE)
  • University of Bremen (DE)
  • University College London (GB)
  • Imperial College London (GB)
Corresponding author
Kassahun, Yohannes
ORCID
Venue
International Journal of Computer Assisted Radiology and Surgery
Publisher
Springer Science and Business Media LLC
Published
2015-10-08
Volume
11
Issue
4
Pages
553-568
ISSN
  • 1861-6410 (ISSN-L)
  • 1861-6429
DOI
10.1007/s11548-015-1305-z
OpenAlex
W2262715205
Cited by
238
References
1
Citations by year
2026: 10 · 2025: 21 · 2024: 29 · 2023: 28 · 2022: 31 · 2021: 35 · 2020: 34 · 2019: 22 · 2018: 13 · 2017: 9 · 2016: 5
FWCI
1.68
Type
journal article
Language
en
Update notices
correction (2016-01-11)
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
  • International Journal of Computer Assisted Radiology and Surgery (publishedVersion)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • Lirias (submittedVersion)
Funders
Seventh Framework Programme (BE)
Topics
  • Health Sciences › Medicine › Surgery › Surgical Simulation and Training
  • Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
Keywords
surgical robotics, surgical skill assessment, surgical skills training, surgical training, learning from human demonstrations
MeSH terms
Machine Learning, Equipment Design, Humans, Robotics

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