Evidence map›Paper›PMID 26450107›Full record

ReviewInternational journal of computer assisted radiology and surgery2016

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

Yohannes Kassahun, Bingbin Yu, Abraham Temesgen Tibebu, Danail Stoyanov, Stamatia Giannarou, Jan Hendrik Metzen, Emmanuel Vander Poorten

Erratum issuedAbstract readReview
PubMed Publisher
In one paragraph

Review in International journal of computer assisted radiology and surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 55 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

55 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  14. [Application and research progress of artificial intelligence technology in trauma treatment].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2023
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  20. Artificial intelligence and automation in endoscopy and surgery.Nature reviews. Gastroenterology & hepatology · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Yohannes KassahunRobotics Innovation Center, German Research Center for Artificial Intelligence, Robert-Hooke-Str. 1, 28359, Bremen, Germany. yohannes.kassahun@dfki.de.
Bingbin YuFaculty 3 - Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 1, 28359, Bremen, Germany.
Abraham Temesgen TibebuFaculty 3 - Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 1, 28359, Bremen, Germany.
Danail StoyanovCentre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
Stamatia GiannarouHamlyn Centre for Robotic Surgery, Imperial College London, London, UK.
Jan Hendrik MetzenFaculty 3 - Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 1, 28359, Bremen, Germany.
Emmanuel Vander PoortenDepartment of Mechanical Engineering, University of Leuven, Celestijnenlaan 300B, 3001, Heverlee, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAdvances 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.

methodsThe 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.

resultsStudies 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.

conclusionML 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.

Indexed as

Machine LearningEquipment DesignHumansRoboticsLearning to perceiveSkill analysisSkill learningSurgical robotics

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.