DeepAI AI Chat
Log In Sign Up

Recurrent and Spiking Modeling of Sparse Surgical Kinematics

by   Neil Getty, et al.
Argonne National Laboratory
University of Illinois at Chicago

Robot-assisted minimally invasive surgery is improving surgeon performance and patient outcomes. This innovation is also turning what has been a subjective practice into motion sequences that can be precisely measured. A growing number of studies have used machine learning to analyze video and kinematic data captured from surgical robots. In these studies, models are typically trained on benchmark datasets for representative surgical tasks to assess surgeon skill levels. While they have shown that novices and experts can be accurately classified, it is not clear whether machine learning can separate highly proficient surgeons from one another, especially without video data. In this study, we explore the possibility of using only kinematic data to predict surgeons of similar skill levels. We focus on a new dataset created from surgical exercises on a simulation device for skill training. A simple, efficient encoding scheme was devised to encode kinematic sequences so that they were amenable to edge learning. We report that it is possible to identify surgical fellows receiving near perfect scores in the simulation exercises based on their motion characteristics alone. Further, our model could be converted to a spiking neural network to train and infer on the Nengo simulation framework with no loss in accuracy. Overall, this study suggests that building neuromorphic models from sparse motion features may be a potentially useful strategy for identifying surgeons and gestures with chips deployed on robotic systems to offer adaptive assistance during surgery and training with additional latency and privacy benefits.


page 1

page 3

page 4


Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning

To ensure satisfactory clinical outcomes, surgical skill assessment must...

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

With the advent of robot-assisted surgery, the role of data-driven appro...

Pose Estimation For Surgical Training

Purpose: This research aims to facilitate the use of state-of-the-art co...

Kinematic Data-Based Action Segmentation for Surgical Applications

Action segmentation is a challenging task in high-level process analysis...

A real-time spatiotemporal AI model analyzes skill in open surgical videos

Open procedures represent the dominant form of surgery worldwide. Artifi...

Analysis of Executional and Procedural Errors in Dry-lab Robotic Surgery Experiments

Background We aim to develop a method for automated detection of potenti...