ArticleInternational journal of computer assisted radiology and surgery2020
Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Software.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Assessing effectiveness and skill transferability in multi-platform simulated training for robotic surgical skills: a systematic review.Journal of robotic surgery · 2025Pooled it
- Measures of performance and proficiency in robotic assisted surgery: a systematic review.Journal of robotic surgery · 2024Pooled it
- [Application of artificial intelligence for surgical skill assessment and quality control in minimally invasive surgery: progress, problems and prospect].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- Artificial intelligence-based expert trajectory guidance in anFrontiers in surgery · 2026Article
- Machine Learning-Based Automated Assessment of Intracorporeal Suturing in Laparoscopic Fundoplication.Global surgical education : journal of the Association for Surgical Education · 2025Article
- Objective performance indicators versus GEARS: an opportunity for more accurate assessment of surgical skill.International journal of computer assisted radiology and surgery · 2024Article
- Depth over RGB: automatic evaluation of open surgery skills using depth camera.International journal of computer assisted radiology and surgery · 2024Article
- Simulated outcomes for durotomy repair in minimally invasive spine surgery.Scientific data · 2024Article
- Effectiveness of a vision-based handle trajectory monitoring system in studying robotic suture operation.Journal of robotic surgery · 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
- Simulator Fidelity Does Not Affect Training for Robot-Assisted Minimally Invasive Surgery.Journal of clinical medicine · 2023Article
- Development and Validation of a Virtual Reality Simulator for Robot-Assisted Minimally Invasive Liver Surgery Training.Journal of clinical medicine · 2022Article
- Endoscopic Image-Based Skill Assessment in Robot-Assisted Minimally Invasive Surgery.Sensors (Basel, Switzerland) · 2021Article
- Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set II: learning curve analysis.International journal of computer assisted radiology and surgery · 2021Article
- Applications of Haptic Technology, Virtual Reality, and Artificial Intelligence in Medical Training During the COVID-19 Pandemic.Frontiers in robotics and AI · 2021Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeThe JIGSAWS dataset is a fixed dataset of robot-assisted surgery kinematic data used to develop predictive models of skill. The purpose of this study is to analyze the relationships of self-defined skill level with global rating scale scores and kinematic data (time, path length and movements) from three exercises (suturing, knot-tying and needle passing) (right and left hands) in the JIGSAWS dataset.
methodsGlobal rating scale scores are reported in the JIGSAWS dataset and kinematic data were calculated using ROVIMAS software. Self-defined skill levels are in the dataset (novice, intermediate, expert). Correlation coefficients (global rating scale-skill level and global rating scale-kinematic parameters) were calculated. Kinematic parameters were compared among skill levels.
resultsGlobal rating scale scores correlated with skill in the knot-tying exercise (r = 0.55, p = 0.0005). In the suturing exercise, time, path length (left) and movements (left) were significantly different (p < 0.05) for novices and experts. For knot-tying, time, path length (right and left) and movements (right) differed significantly for novices and experts. For needle passing, no kinematic parameter was significantly different comparing novices and experts. The only kinematic parameter that correlated with global rating scale scores is time in the knot-tying exercise.
conclusionGlobal rating scale scores weakly correlate with skill level and kinematic parameters. The ability of kinematic parameters to differentiate among self-defined skill levels is inconsistent. Additional data are needed to enhance the dataset and facilitate subset analyses and future model development.
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