Assessing unconstrained surgical cuttings in VR using CNNs

TL;DR

This paper introduces a CNN-based method to evaluate unconstrained surgical cuttings in virtual reality, utilizing a dataset enhanced through data augmentation to improve assessment accuracy.

Contribution

The study develops a CNN model specifically designed for assessing surgical cuttings in VR, with a novel dataset created via data augmentation techniques.

Findings

Effective CNN model for surgical cutting assessment

Enhanced dataset improves model robustness

Potential for real-time surgical training feedback

Abstract

We present a Convolutional Neural Network (CNN) suitable to assess unconstrained surgical cuttings, trained on a dataset created with a data augmentation technique.