Evidence map›Paper›PMID 38872018›Full record

ArticleSurgical endoscopy2024

A surgical activity model of laparoscopic cholecystectomy for co-operation with collaborative robots.

R Younis, A Yamlahi, S Bodenstedt, P M Scheikl, A Kisilenko, M Daum, A Schulze, P A Wise, F Nickel, F Mathis-Ullrich and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Surgical endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Modular instrument actuation unit for robotic-assisted systems in laparoscopic surgery.International journal of computer assisted radiology and surgery · 2026
    Article
  5. Article
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

R YounisDepartment for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany.
A YamlahiDivision of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany.
S BodenstedtDepartment for Translational Surgical Oncology, National Center for Tumor Diseases, Partner Site Dresden, Dresden, Germany.
P M ScheiklSurgical Planning and Robotic Cognition (SPARC), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
A KisilenkoDepartment for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany.
M DaumCentre for the Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology, Dresden, Germany.
A SchulzeCentre for the Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology, Dresden, Germany.
P A WiseDepartment for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany.
F NickelDepartment for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany.
F Mathis-UllrichSurgical Planning and Robotic Cognition (SPARC), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
L Maier-HeinNational Center for Tumor Diseases (NCT), Heidelberg, Germany.
B P Müller-StichDepartment for Abdominal Surgery, University Center for Gastrointestinal and Liver Diseases, Basel, Switzerland.
S SpeidelDepartment for Translational Surgical Oncology, National Center for Tumor Diseases, Partner Site Dresden, Dresden, Germany.
M DistlerDepartment of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany.
J WeitzCentre for the Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology, Dresden, Germany.
M WagnerDepartment for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany. martin.wagner@ukdd.de.ORCID 0000-0002-9831-9110

Funding

Bundesministerium für Bildung und Forschung 13GW0471DDeutsche Forschungsgemeinschaft 390696704
6 · The paper itself

Abstract

backgroundLaparoscopic 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. To achieve context-awareness for surgeon-robot collaboration, the intraoperative action workflow recognition is a key challenge.

methodsA surgical process model was developed for intraoperative surgical activities including actor, instrument, action and target in laparoscopic cholecystectomy (excluding camera guidance). These activities, as well as instrument presence and surgical phases were annotated in videos of laparoscopic cholecystectomy performed on human patients (n = 10) and on explanted porcine livers (n = 10). The machine learning algorithm Distilled-Swin was trained on our own annotated dataset and the CholecT45 dataset. The validation of the model was conducted using a fivefold cross-validation approach.

resultsIn total, 22,351 activities were annotated with a cumulative duration of 24.9 h of video segments. The machine learning algorithm trained and validated on our own dataset scored a mean average precision (mAP) of 25.7% and a top K = 5 accuracy of 85.3%. With training and validation on our dataset and CholecT45, the algorithm scored a mAP of 37.9%.

conclusionsAn activity model was developed and applied for the fine-granular annotation of laparoscopic cholecystectomies in two surgical settings. A machine recognition algorithm trained on our own annotated dataset and CholecT45 achieved a higher performance than training only on CholecT45 and can recognize frequently occurring activities well, but not infrequent activities. The analysis of an annotated dataset allowed for the quantification of the potential of collaborative surgical robots to address the workload of surgical staff. If collaborative surgical robots could grasp and hold tissue, up to 83.5% of the assistant's tissue interacting tasks (i.e. excluding camera guidance) could be performed by robots.

Indexed as

Cholecystectomy, LaparoscopicMachine LearningRobotic Surgical ProceduresAlgorithmsAnimalsHumansSwineVideo RecordingWorkflowAction recognitionCollaborative surgical robotsMachine learningRobot autonomySurgical data scienceSurgical process modeling

Identifiers

PMID38872018
PMCPMC11289174

What OpenQuestion holds

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LicenceCC BY
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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.