# Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks

arXiv: https://arxiv.org/abs/1908.07319 · HTML: https://mlsanity.com/paper/1908.07319/

## Key facts

- Cited by 18 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 20 Aug 2019.

## Built on by (most cited first, 18 of 18)

- [Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey](https://mlsanity.com/paper/2104.00950/) (2021)
- [Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark](https://mlsanity.com/paper/2109.14956/) (2021)
- [Interpretation of Time-Series Deep Models: A Survey](https://mlsanity.com/paper/2305.14582/) (2023)
- [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)
- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Robust Explainer Recommendation for Time Series Classification](https://mlsanity.com/paper/2306.05501/) (2023)
- [Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification](https://mlsanity.com/paper/2406.12507/) (2024)
- [Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series](https://mlsanity.com/paper/2203.07861/) (2022)
- [Think Step by Step: Chain-of-Gesture Prompting for Error Detection in Robotic Surgical Videos](https://mlsanity.com/paper/2406.19217/) (2024)
- [Uncertainty-aware Self-supervised Learning for Cross-domain Technical Skill Assessment in Robot-assisted Surgery](https://mlsanity.com/paper/2304.14589/) (2023)
- [Machine Learning-Based Automated Assessment of Intracorporeal Suturing in Laparoscopic Fundoplication](https://mlsanity.com/paper/2412.16195/) (2024)
- [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)
- [Human Gaze Guided Attention for Surgical Activity Recognition](https://mlsanity.com/paper/2203.04752/) (2022)
- [ReCAP: Recursive Cross Attention Network for Pseudo-Label Generation in Robotic Surgical Skill Assessment](https://mlsanity.com/paper/2407.05180/) (2024)
- [Cognitive-Motor Integration in Assessing Bimanual Motor Skills](https://mlsanity.com/paper/2404.10889/) (2024)
- [SurgFusion-Net: Diversified Adaptive Multimodal Fusion Network for Surgical Skill Assessment](https://mlsanity.com/paper/2603.00108/) (2026)
- [ExpOS: Explainable Open-Surgery Skills Assessment Using 3D Hand Reconstruction](https://mlsanity.com/paper/2605.23653/) (2026)

## Abstract (arXiv)

Purpose: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of trainees' surgical skills in order to improve surgical practice. Methods: In this paper, we designed a convolutional neural network (CNN) to classify surgical skills by extracting latent patterns in the trainees' motions performed during robotic surgery. The method is validated on the JIGSAWS dataset for two surgical skills evaluation tasks: classification and regression. Results: Our results show that deep neural networks constitute robust machine learning models that are able to reach new competitive state-of-the-art performance on the JIGSAWS dataset. While we leveraged from CNNs' efficiency, we were able to minimize its black-box effect using the class activation map technique. Conclusions: This characteristic allowed our method to automatically pinpoint which parts of the surgery influenced the skill evaluation the most, thus allowing us to explain a surgical skill classification and provide surgeons with a novel personalized feedback technique. We believe this type of interpretable machine learning model could integrate within "Operation Room 2.0" and support novice surgeons in improving their skills to eventually become experts.