Evidence map›Paper›PMID 40319437›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

Towards automatic quantification of operating table interaction in operating rooms.

Rick M Butler, Anne M Schouten, Anne C van der Eijk, Maarten van der Elst, Benno H W Hendriks, John J van den Dobbelsteen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

6 authors.

Rick M ButlerDelft University of Technology, Delft, the Netherlands. r.m.butler@tudelft.nl.ORCID http://orcid.org/0000-0002-3202-9401
Anne M SchoutenDelft University of Technology, Delft, the Netherlands.ORCID http://orcid.org/0000-0003-2959-9443
Anne C van der EijkDelft University of Technology, Delft, the Netherlands.ORCID http://orcid.org/0000-0002-6565-6874
Maarten van der ElstDelft University of Technology, Delft, the Netherlands.
Benno H W HendriksDelft University of Technology, Delft, the Netherlands.ORCID http://orcid.org/0009-0007-5854-4552
John J van den DobbelsteenDelft University of Technology, Delft, the Netherlands.ORCID http://orcid.org/0000-0003-3423-1707

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePerioperative staff shortages are a problem in hospitals worldwide. Keeping the staff content and motivated is a challenge in the busy hospital setting of today. New operating room technologies aim to increase safety and efficiency. This causes a shift from interaction with patients to interaction with technology. Objectively measuring this shift could aid the design of supportive technological products, or optimal planning for high-tech procedures.

methods35 Gynaecological procedures of three different technology levels are recorded: open- (OS), minimally invasive- (MIS) and robot-assisted (RAS) surgery. We annotate interaction between staff and the patient. An algorithm is proposed that detects interaction with the operating table from staff posture and movement. Interaction is expressed as a percentage of total working time.

resultsThe proposed algorithm measures operating table interactions of 70.4%, 70.3% and 30.1% during OS, MIS and RAS. Annotations yield patient interaction percentages of 37.6%, 38.3% and 24.6%. Algorithm measurements over time show operating table and patient interaction peaks at anomalous events or workflow phase transitions.

conclusionsThe annotations show less operating table and patient interactions during RAS than OS and MIS. Annotated patient interaction and measured operating table interaction show similar differences between procedures and workflow phases. The visual complexity of operating rooms complicates pose tracking, deteriorating the algorithm input quality. The proposed algorithm shows promise as a component in context-aware event- or workflow phase detection.

Indexed as

Gynecologic Surgical ProceduresOperating RoomsOperating TablesRobotic Surgical ProceduresAlgorithmsFemaleHumansMinimally Invasive Surgical ProceduresWorkflowCamera monitoringHuman pose trackingPerioperative processRobot-assisted surgerySurgical workflowWorkload

Identifiers

PMID40319437
PMCPMC12518443

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.