Evidence map›Paper›PMID 38733021›Full record

ArticleSensors (Basel, Switzerland)2024

Assessment of Surgeons' Stress Levels with Digital Sensors during Robot-Assisted Surgery: An Experimental Study.

Kristóf Takács, Eszter Lukács, Renáta Levendovics, Damján Pekli, Attila Szijártó, Tamás Haidegger

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. MiDAS: A Multimodal Data Acquisition System and Dataset for Robot-Assisted Minimally Invasive Surgery.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026
    Article
  2. Cross-Stream and Cross-Channel Attention Networks for Surgical Skill Classification in Open Surgery From Hand Kinematics.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026
    Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. [Robot-assisted esophageal surgery].Magyar onkologia · 2024
    Review
  9. Article
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.

Kristóf TakácsAntal Bejczy Center for Intelligent Robotics (IROB), University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary.ORCID 0000-0001-5417-6026
Eszter LukácsAntal Bejczy Center for Intelligent Robotics (IROB), University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary.ORCID 0009-0001-7585-281X
Renáta LevendovicsAntal Bejczy Center for Intelligent Robotics (IROB), University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary.ORCID 0000-0002-3030-254X
Damján PekliDepartment of Surgery, Transplantation and Gastroenterology, Semmelweis University, 1082 Budapest, Hungary.ORCID 0000-0002-4820-5625
Attila SzijártóDepartment of Surgery, Transplantation and Gastroenterology, Semmelweis University, 1082 Budapest, Hungary.ORCID 0000-0003-3768-4912
Tamás HaideggerAntal Bejczy Center for Intelligent Robotics (IROB), University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary.ORCID 0000-0003-1402-1139

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robot-Assisted Minimally Invasive Surgery (RAMIS) marks a paradigm shift in surgical procedures, enhancing precision and ergonomics. Concurrently it introduces complex stress dynamics and ergonomic challenges regarding the human-robot interface and interaction. This study explores the stress-related aspects of RAMIS, using the da Vinci XI Surgical System and the Sea Spikes model as a standard skill training phantom to establish a link between technological advancement and human factors in RAMIS environments. By employing different physiological and kinematic sensors for heart rate variability, hand movement tracking, and posture analysis, this research aims to develop a framework for quantifying the stress and ergonomic loads applied to surgeons. Preliminary findings reveal significant correlations between stress levels and several of the skill-related metrics measured by external sensors or the SURG-TLX questionnaire. Furthermore, early analysis of this preliminary dataset suggests the potential benefits of applying machine learning for surgeon skill classification and stress analysis. This paper presents the initial findings, identified correlations, and the lessons learned from the clinical setup, aiming to lay down the cornerstones for wider studies in the fields of clinical situation awareness and attention computing.

Indexed as

Robotic Surgical ProceduresSurgeonsBiomechanical PhenomenaErgonomicsHeart RateHumansMachine LearningMaleMinimally Invasive Surgical Proceduresclinical situation awarenesshuman–robot cooperationnon-technical surgical skillsrobot-assisted minimally invasive surgerystress analysis

Identifiers

PMID38733021
PMCPMC11086209

What OpenQuestion holds

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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.