# Evaluating surgical skills from kinematic data using convolutional neural networks

arXiv: https://arxiv.org/abs/1806.02750 · HTML: https://mlsanity.com/paper/1806.02750/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2018.

## Built on by (most cited first, 19 of 19)

- [Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives](https://mlsanity.com/paper/2311.18259/) (2023)
- [Towards Unified Surgical Skill Assessment](https://mlsanity.com/paper/2106.01035/) (2021)
- [MIcro-Surgical Anastomose Workflow recognition challenge report](https://mlsanity.com/paper/2103.13111/) (2021)
- [BASKET: A Large-Scale Video Dataset for Fine-Grained Skill Estimation](https://mlsanity.com/paper/2503.20781/) (2025)
- [Surgical Skill Assessment via Video Semantic Aggregation](https://mlsanity.com/paper/2208.02611/) (2022)
- [Video-based fully automatic assessment of open surgery suturing skills](https://mlsanity.com/paper/2110.13972/) (2021)
- [dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification](https://mlsanity.com/paper/2207.12165/) (2022)
- [Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review](https://mlsanity.com/paper/2103.05113/) (2021)
- [Look Into the LITE in Deep Learning for Time Series Classification](https://mlsanity.com/paper/2409.02869/) (2024)
- [Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning](https://mlsanity.com/paper/2203.09589/) (2022)
- [Using Hand Pose Estimation To Automate Open Surgery Training Feedback](https://mlsanity.com/paper/2211.07021/) (2022)
- [Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding](https://mlsanity.com/paper/2303.18019/) (2023)
- [Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal](https://mlsanity.com/paper/2308.02433/) (2023)
- [A benchmark for video-based laparoscopic skill analysis and assessment](https://mlsanity.com/paper/2602.09927/) (2026)
- [Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning Approach](https://mlsanity.com/paper/2101.03701/) (2021)
- [Self-supervised Extraction of Human Motion Structures via Frame-wise Discrete Features](https://mlsanity.com/paper/2309.05972/) (2023)
- [Enhancing Surgical Performance in Cardiothoracic Surgery with Innovations from Computer Vision and Artificial Intelligence: A Narrative Review](https://mlsanity.com/paper/2402.11288/) (2024)
- [Cognitive-Motor Integration in Assessing Bimanual Motor Skills](https://mlsanity.com/paper/2404.10889/) (2024)
- [RAUL: Reference-Assisted Ureteroscopy Localization for Skill Assessment](https://mlsanity.com/paper/2609.19236/) (2026)

## Abstract (arXiv)

The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming. Thus, automating surgical skills evaluation is a very important step towards improving surgical practice. In this paper, we designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by extracting patterns in the surgeon motions performed in robotic surgery. The proposed method is validated on the JIGSAWS dataset and achieved very competitive results with 100% accuracy on the suturing and needle passing tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate its black-box effect using class activation map. This feature allows our method to automatically highlight which parts of the surgical task influenced the skill prediction and can be used to explain the classification and to provide personalized feedback to the trainee.