# Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge

arXiv: https://arxiv.org/abs/2212.04448 · HTML: https://mlsanity.com/paper/2212.04448/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 14 of 14)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark](https://mlsanity.com/paper/2109.14956/) (2021)
- [Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks](https://mlsanity.com/paper/1802.08774/) (2018)
- [Video-based surgical skill assessment using 3D convolutional neural networks](https://mlsanity.com/paper/1903.02306/) (2019)
- [BIAS: Transparent reporting of biomedical image analysis challenges](https://mlsanity.com/paper/1910.04071/) (2019)
- [CholecTriplet2021: A benchmark challenge for surgical action triplet recognition](https://mlsanity.com/paper/2204.04746/) (2022)
- [Video and Accelerometer-Based Motion Analysis for Automated Surgical Skills Assessment](https://mlsanity.com/paper/1702.07772/) (2017)
- [Automated Surgical Skill Assessment in RMIS Training](https://mlsanity.com/paper/1712.08604/) (2017)
- [Surgical Activity Recognition in Robot-Assisted Radical Prostatectomy using Deep Learning](https://mlsanity.com/paper/1806.00466/) (2018)
- [Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-assisted Surgery](https://mlsanity.com/paper/1806.05796/) (2018)
- [Surgical Visual Domain Adaptation: Results from the MICCAI 2020 SurgVisDom Challenge](https://mlsanity.com/paper/2102.13644/) (2021)
- [Surgical Data Science: A Consensus Perspective](https://mlsanity.com/paper/1806.03184/) (2018)
- [Novel evaluation of surgical activity recognition models using task-based efficiency metrics](https://mlsanity.com/paper/1907.02060/) (2019)

## Built on by (most cited first, 6 of 6)

- [Surgical Visual Understanding (SurgVU) Dataset](https://mlsanity.com/paper/2501.09209/) (2025)
- [Video Dataset for Surgical Phase, Keypoint, and Instrument Recognition in Laparoscopic Surgery (PhaKIR)](https://mlsanity.com/paper/2511.06549/) (2025)
- [A Decade of Action Quality Assessment: Largest Systematic Survey of Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2502.02817/) (2025)
- [Artificial Intelligence for Biomedical Video Generation](https://mlsanity.com/paper/2411.07619/) (2024)
- [Scaling Video Pretraining for Surgical Foundation Models](https://mlsanity.com/paper/2603.29966/) (2026)
- [Video-based Surgical Skill Assessment Using Dynamics-and-Uncertainty-Aware Tree-based Gaussian Process Classifier](https://mlsanity.com/paper/2609.24619/) (2026)

## Abstract (arXiv)

Timely and effective feedback within surgical training plays a critical role in developing the skills required to perform safe and efficient surgery. Feedback from expert surgeons, while especially valuable in this regard, is challenging to acquire due to their typically busy schedules, and may be subject to biases. Formal assessment procedures like OSATS and GEARS attempt to provide objective measures of skill, but remain time-consuming. With advances in machine learning there is an opportunity for fast and objective automated feedback on technical skills. The SimSurgSkill 2021 challenge (hosted as a sub-challenge of EndoVis at MICCAI 2021) aimed to promote and foster work in this endeavor. Using virtual reality (VR) surgical tasks, competitors were tasked with localizing instruments and predicting surgical skill. Here we summarize the winning approaches and how they performed. Using this publicly available dataset and results as a springboard, future work may enable more efficient training of surgeons with advances in surgical data science. The dataset can be accessed from https://console.cloud.google.com/storage/browser/isi-simsurgskill-2021.