Force classification during robotic interventions through simulation-trained neural networks.
Journal:
International journal of computer assisted radiology and surgery
PMID:
31420832
Abstract
PURPOSE: Intravitreal injection is among the most frequent treatment strategies for chronic ophthalmic diseases. The last decade has seen a serious increase in the number of intravitreal injections, and with it, adverse effects and drawbacks. To tackle these problems, medical assistive devices for robotized injections have been suggested and are projected to enhance delivery mechanisms for a new generation of pharmacological solutions. In this paper, we present a method aimed at improving the safety characteristics of upcoming robotic systems. Our vision-based method uses a combination of 2D OCT data, numerical simulation and machine learning to classify the range of the force applied by an injection needle on the sclera.
Authors
Keywords
Animals
Bayes Theorem
Computer Simulation
Diagnostic Techniques, Ophthalmological
Humans
Image Processing, Computer-Assisted
Intravitreal Injections
Machine Learning
Mechanical Phenomena
Models, Theoretical
Neural Networks, Computer
Reproducibility of Results
Robotic Surgical Procedures
Sclera
Swine
Tomography, Optical Coherence