# Offline identification of surgical deviations in laparoscopic rectopexy

arXiv: https://arxiv.org/abs/1909.10790 · HTML: https://mlsanity.com/paper/1909.10790/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 12 of 12)

- [Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos](https://mlsanity.com/paper/2109.03223/) (2021)
- [Temporal Memory Relation Network for Workflow Recognition from Surgical Video](https://mlsanity.com/paper/2103.16327/) (2021)
- [OperA: Attention-Regularized Transformers for Surgical Phase Recognition](https://mlsanity.com/paper/2103.03873/) (2021)
- [MIcro-Surgical Anastomose Workflow recognition challenge report](https://mlsanity.com/paper/2103.13111/) (2021)
- [PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition?](https://mlsanity.com/paper/2202.05821/) (2022)
- [Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study](https://mlsanity.com/paper/2203.09230/) (2022)
- [GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos](https://mlsanity.com/paper/2307.11081/) (2023)
- [SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba](https://mlsanity.com/paper/2409.12108/) (2024)
- [TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation](https://mlsanity.com/paper/2502.06708/) (2025)
- [Hierarchical Context Transformer for Multi-level Semantic Scene Understanding](https://mlsanity.com/paper/2502.15184/) (2025)
- [CPKD: Clinical Prior Knowledge-Constrained Diffusion Models for Surgical Phase Recognition in Endoscopic Submucosal Dissection](https://mlsanity.com/paper/2507.03295/) (2025)
- [ProtoFlow: Interpretable and Robust Surgical Workflow Modeling with Learned Dynamic Scene Graph Prototypes](https://mlsanity.com/paper/2512.14092/) (2025)

## Abstract (arXiv)

Objective: A median of 14.4% of patient undergone at least one adverse event during surgery and a third of them are preventable. The occurrence of adverse events forces surgeons to implement corrective strategies and, thus, deviate from the standard surgical process. Therefore, it is clear that the automatic identification of adverse events is a major challenge for patient safety. In this paper, we have proposed a method enabling us to identify such deviations. We have focused on identifying surgeons' deviations from standard surgical processes due to surgical events rather than anatomic specificities. This is particularly challenging, given the high variability in typical surgical procedure workflows. Methods: We have introduced a new approach designed to automatically detect and distinguish surgical process deviations based on multi-dimensional non-linear temporal scaling with a hidden semi-Markov model using manual annotation of surgical processes. The approach was then evaluated using cross-validation. Results: The best results have over 90% accuracy. Recall and precision were superior at 70%. We have provided a detailed analysis of the incorrectly-detected observations. Conclusion: Multi-dimensional non-linear temporal scaling with a hidden semi-Markov model provides promising results for detecting deviations. Our error analysis of the incorrectly-detected observations offers different leads in order to further improve our method. Significance: Our method demonstrated the feasibility of automatically detecting surgical deviations that could be implemented for both skill analysis and developing situation awareness-based computer-assisted surgical systems.