# SATR-DL: Improving Surgical Skill Assessment and Task Recognition in Robot-assisted Surgery with Deep Neural Networks

arXiv: https://arxiv.org/abs/1806.05798 · HTML: https://mlsanity.com/paper/1806.05798/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2018.

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

- [Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review](https://mlsanity.com/paper/2103.05113/) (2021)
- [Uncertainty-aware Self-supervised Learning for Cross-domain Technical Skill Assessment in Robot-assisted Surgery](https://mlsanity.com/paper/2304.14589/) (2023)
- [Predicting the Timing of Camera Movements From the Kinematics of Instruments in Robotic-Assisted Surgery Using Artificial Neural Networks](https://mlsanity.com/paper/2109.11192/) (2021)
- [SURGIVID: Annotation-Efficient Surgical Video Object Discovery](https://mlsanity.com/paper/2409.07801/) (2024)
- [AI-Driven Evaluation of Surgical Skill via Action Recognition](https://mlsanity.com/paper/2512.24411/) (2025)
- [An Integrated Video-AI Platform for Action-Level Microanastomosis Training and Performance Feedback](https://mlsanity.com/paper/2609.06380/) (2026)

## Abstract (arXiv)

Purpose: This paper focuses on an automated analysis of surgical motion profiles for objective skill assessment and task recognition in robot-assisted surgery. Existing techniques heavily rely on conventional statistic measures or shallow modelings based on hand-engineered features and gesture segmentation. Such developments require significant expert knowledge, are prone to errors, and are less efficient in online adaptive training systems. Methods: In this work, we present an efficient analytic framework with a parallel deep learning architecture, SATR-DL, to assess trainee expertise and recognize surgical training activity. Through an end-to-end learning technique, abstract information of spatial representations and temporal dynamics is jointly obtained directly from raw motion sequences. Results: By leveraging a shared high-level representation learning, the resulting model is successful in the recognition of trainee skills and surgical tasks, suturing, needle-passing, and knot-tying. Meanwhile, we explore the use of ensemble in classification at the trial level, where the SATR-DL outperforms state-of-the-art performance by achieving accuracies of 0.960 and 1.000 in skill assessment and task recognition, respectively. Conclusion: This study highlights the potential of SATR-DL to provide improvements for an efficient data-driven assessment in intelligent robotic surgery.