# Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-assisted Surgery

arXiv: https://arxiv.org/abs/1806.05796 · HTML: https://mlsanity.com/paper/1806.05796/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2018.

## Built on by (most cited first, 17 of 17)

- [Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark](https://mlsanity.com/paper/2109.14956/) (2021)
- [Towards Unified Surgical Skill Assessment](https://mlsanity.com/paper/2106.01035/) (2021)
- [Surgical Skill Assessment via Video Semantic Aggregation](https://mlsanity.com/paper/2208.02611/) (2022)
- [Recognition and Prediction of Surgical Gestures and Trajectories Using Transformer Models in Robot-Assisted Surgery](https://mlsanity.com/paper/2212.01683/) (2022)
- [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)
- [Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection](https://mlsanity.com/paper/2312.06295/) (2023)
- [Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge](https://mlsanity.com/paper/2212.04448/) (2022)
- [Runtime Detection of Executional Errors in Robot-Assisted Surgery](https://mlsanity.com/paper/2203.00737/) (2022)
- [Uncertainty-aware Self-supervised Learning for Cross-domain Technical Skill Assessment in Robot-assisted Surgery](https://mlsanity.com/paper/2304.14589/) (2023)
- [Enhancing Precision in Tactile Internet-Enabled Remote Robotic Surgery: Kalman Filter Approach](https://mlsanity.com/paper/2406.04503/) (2024)
- [A benchmark for video-based laparoscopic skill analysis and assessment](https://mlsanity.com/paper/2602.09927/) (2026)
- [MiDAS: A Multimodal Data Acquisition System and Dataset for Robot-Assisted Minimally Invasive Surgery](https://mlsanity.com/paper/2602.12407/) (2026)
- [Automatic Detection of Out-of-body Frames in Surgical Videos for Privacy Protection Using Self-supervised Learning and Minimal Labels](https://mlsanity.com/paper/2303.18106/) (2023)
- [Explainable AI for Automated User-specific Feedback in Surgical Skill Acquisition](https://mlsanity.com/paper/2508.02593/) (2025)
- [RAUL: Reference-Assisted Ureteroscopy Localization for Skill Assessment](https://mlsanity.com/paper/2609.19236/) (2026)
- [Semantic RGB--Depth Based Surgical Skill Assessment in Microscopic Stereo Videos](https://mlsanity.com/paper/2610.01205/) (2026)

## Abstract (arXiv)

With the advent of robot-assisted surgery, the role of data-driven approaches to integrate statistics and machine learning is growing rapidly with prominent interests in objective surgical skill assessment. However, most existing work requires translating robot motion kinematics into intermediate features or gesture segments that are expensive to extract, lack efficiency, and require significant domain-specific knowledge. We propose an analytical deep learning framework for skill assessment in surgical training. A deep convolutional neural network is implemented to map multivariate time series data of the motion kinematics to individual skill levels. We perform experiments on the public minimally invasive surgical robotic dataset, JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). Our proposed learning model achieved a competitive accuracy of 92.5%, 95.4%, and 91.3%, in the standard training tasks: Suturing, Needle-passing, and Knot-tying, respectively. Without the need of engineered features or carefully-tuned gesture segmentation, our model can successfully decode skill information from raw motion profiles via end-to-end learning. Meanwhile, the proposed model is able to reliably interpret skills within 1-3 second window, without needing an observation of entire training trial. This study highlights the potentials of deep architectures for an proficient online skill assessment in modern surgical training.