ArticleSurgical endoscopy2017
Analysis of motion in laparoscopy: the deconstruction of an intra-corporeal suturing task.
Article in Surgical endoscopy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04646304 (Motion Capture as a Tool for the Assessment of Laparoscopic Performance), which is not on this map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Motion Capture as a Tool for the Assessment of Laparoscopic Performance
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation methods and impact of simulation-based training in pediatric surgery: a systematic review.Pediatric surgery international · 2019Pooled it
- Hand Motion Analysis of Different Segments of a Procedure: Is One Segment Enough?Journal of medical systems · 2025Article
- Surgeon Upper Extremity Kinematics During Error and Error-Free Retropubic Trocar Passage.International urogynecology journal · 2024Article
- Development of a synchronous motion-tracking and video capture tool for flexible ureteroscopy.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2024Article
- Baseline performance in a robotic virtual reality platform predicts rate of skill acquisition in a proficiency-based curriculum: a cohort study of surgical trainees.Surgical endoscopy · 2023Article
- An Automated Skill Assessment Framework Based on Visual Motion Signals and a Deep Neural Network in Robot-Assisted Minimally Invasive Surgery.Sensors (Basel, Switzerland) · 2023Article
- Application of a Perception NeuronJournal of clinical medicine · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study analyzes instrument motion for segments of a defined intra-corporeal suturing task in a laparoscopic simulator. We describe a system providing real-time velocity and acceleration assessment in the performance of this task. Analysis of the deconstructed task segments allows targeted assessment and teaching.
methodsA traditional box trainer was fitted with a custom-built motion-tracking system. Participants were stratified into novice, intermediate and expert groups. They performed a defined intra-corporeal suturing task. Real-time data were collected in four degrees of freedom (DOFs) (Roll, Surge, Pitch, Yaw). The task was then deconstructed into four segments: loading needle/pull-through, double-throw knot, first single-throw knot, and second single-throw knot. Motion analysis parameters (MAPs) were studied for each DOF.
resultsSixty-four participants were tested (14 novices, 19 intermediates, 31 experts). The largest difference in MAPs was seen in the 'double-throw knot' segment. MAPs for the 'loading needle/pull-through' segment revealed differences between novices and experts in Roll and Pitch DOFs only. For the 'first single knot' segment, similar MAP trends were noted across all DOFs, with significant differences between novices versus experts and intermediates versus experts. For the 'second single knot' segment, the difference in MAPs was preserved only for novices versus experts.
conclusionsBy analyzing motion for a defined suturing task in a laparoscopic simulator, we can gain insight into the specific hand motions distinguishing experts from non-experts. Such information may allow teaching in a more focused, effective and efficient manner.
Indexed as
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Registered trials
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.