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

TL;DR

This paper introduces a deep convolutional neural network that accurately assesses surgical skills directly from raw motion data, enabling efficient, real-time evaluation without complex feature engineering or gesture segmentation.

Contribution

The study presents an end-to-end deep learning framework that bypasses traditional feature extraction, achieving high accuracy in objective surgical skill assessment from raw kinematic data.

Findings

Achieved over 92% accuracy in skill assessment tasks.

Successfully decoded skills within 1-3 second windows.

Eliminated need for gesture segmentation and feature engineering.

Abstract

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.