Assessing unconstrained surgical cuttings in VR using CNNs
Ilias Chrysovergis, Manos Kamarianakis, Mike Kentros, Dimitris Angelis, Antonis Protopsaltis, George Papagiannakis
arXiv:2205.00934v11 citationsh-index: 31
Originality Synthesis-oriented
AI Analysis
This addresses the need for automated assessment in surgical training simulations, but appears incremental as it applies an existing method (CNN) to a new domain (VR surgical cuttings).
The paper tackles the problem of assessing unconstrained surgical cuttings in virtual reality (VR) by presenting a Convolutional Neural Network (CNN) trained on a dataset created with data augmentation, but no concrete results or numbers are provided in the abstract.
We present a Convolutional Neural Network (CNN) suitable to assess unconstrained surgical cuttings, trained on a dataset created with a data augmentation technique.