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

by   Hassan Ismail Fawaz, et al.

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.


page 1

page 2

page 3

page 4


Evaluating surgical skills from kinematic data using convolutional neural networks

The need for automatic surgical skills assessment is increasing, especia...

Video-based surgical skill assessment using 3D convolutional neural networks

Purpose: A profound education of novice surgeons is crucial to ensure th...

Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge

Timely and effective feedback within surgical training plays a critical ...

Real-time Informative Surgical Skill Assessment with Gaussian Process Learning

Endoscopic Sinus and Skull Base Surgeries (ESSBSs) is a challenging and ...

Assessing unconstrained surgical cuttings in VR using CNNs

We present a Convolutional Neural Network (CNN) suitable to assess uncon...

Surgical task expertise detected by a self-organizing neural network map

Individual grip force profiling of bimanual simulator task performance o...

Automatic alignment of surgical videos using kinematic data

Over the past one hundred years, the classic teaching methodology of "se...

Code Repositories


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

view repo

Please sign up or login with your details

Forgot password? Click here to reset