# Analysis of Executional and Procedural Errors in Dry-lab Robotic Surgery Experiments

arXiv: https://arxiv.org/abs/2106.11962 · HTML: https://mlsanity.com/paper/2106.11962/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2021.

## Built on by (most cited first, 7 of 7)

- [Runtime Detection of Executional Errors in Robot-Assisted Surgery](https://mlsanity.com/paper/2203.00737/) (2022)
- [Think Step by Step: Chain-of-Gesture Prompting for Error Detection in Robotic Surgical Videos](https://mlsanity.com/paper/2406.19217/) (2024)
- [Towards Surgical Context Inference and Translation to Gestures](https://mlsanity.com/paper/2302.14237/) (2023)
- [SEDMamba: Enhancing Selective State Space Modelling with Bottleneck Mechanism and Fine-to-Coarse Temporal Fusion for Efficient Error Detection in Robot-Assisted Surgery](https://mlsanity.com/paper/2406.15920/) (2024)
- [Robotic Scene Segmentation with Memory Network for Runtime Surgical Context Inference](https://mlsanity.com/paper/2308.12789/) (2023)
- [MiDAS: A Multimodal Data Acquisition System and Dataset for Robot-Assisted Minimally Invasive Surgery](https://mlsanity.com/paper/2602.12407/) (2026)
- [Real-Time Multimodal Activity-Aware Error Detection in Robot-Assisted Surgery](https://mlsanity.com/paper/2606.23593/) (2026)

## Abstract (arXiv)

Background Analyzing kinematic and video data can help identify potentially erroneous motions that lead to sub-optimal surgeon performance and safety-critical events in robot-assisted surgery. Methods We develop a rubric for identifying task and gesture-specific Executional and Procedural errors and evaluate dry-lab demonstrations of Suturing and Needle Passing tasks from the JIGSAWS dataset. We characterize erroneous parts of demonstrations by labeling video data, and use distribution similarity analysis and trajectory averaging on kinematic data to identify parameters that distinguish erroneous gestures. Results Executional error frequency varies by task and gesture, and correlates with skill level. Some predominant error modes in each gesture are distinguishable by analyzing error-specific kinematic parameters. Procedural errors could lead to lower performance scores and increased demonstration times but also depend on surgical style. Conclusions This study provides insights into context-dependent errors that can be used to design automated error detection mechanisms and improve training and skill assessment.