# Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery

arXiv: https://arxiv.org/abs/1611.05136 · HTML: https://mlsanity.com/paper/1611.05136/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2016.

## Built on by (most cited first, 5 of 5)

- [Video-based fully automatic assessment of open surgery suturing skills](https://mlsanity.com/paper/2110.13972/) (2021)
- [Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review](https://mlsanity.com/paper/2103.05113/) (2021)
- [Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning](https://mlsanity.com/paper/2203.09589/) (2022)
- [Machine Learning-Based Automated Assessment of Intracorporeal Suturing in Laparoscopic Fundoplication](https://mlsanity.com/paper/2412.16195/) (2024)
- [Mapping Surgeon's Hand/Finger Motion During Conventional Microsurgery to Enhance Intuitive Surgical Robot Teleoperation](https://mlsanity.com/paper/2102.10585/) (2021)

## Abstract (arXiv)

Evaluating surgeon skill has predominantly been a subjective task. Development of objective methods for surgical skill assessment are of increased interest. Recently, with technological advances such as robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and automated assessment frameworks have arisen. In this paper, we applied machine learning methods to automatically evaluate performance of the surgeon in RMIS. Six important movement features were used in the evaluation including completion time, path length, depth perception, speed, smoothness and curvature. Different classification methods applied to discriminate expert and novice surgeons. We test our method on real surgical data for suturing task and compare the classification result with the ground truth data (obtained by manual labeling). The experimental results show that the proposed framework can classify surgical skill level with relatively high accuracy of 85.7%. This study demonstrates the ability of machine learning methods to automatically classify expert and novice surgeons using movement features for different RMIS tasks. Due to the simplicity and generalizability of the introduced classification method, it is easy to implement in existing trainers.