Quality-dependent Deep Learning for Safe Autonomous Guidewire Navigation

Cardiovascular diseases are the main cause of death worldwide. State-of-the-art treatment often includes the process of navigating endovascular instruments through the vasculature. Automation of the procedure receives much attention lately and may increase treatment quality and unburden clinicians. However, in order to ensure the patient's safety the endovascular device needs to be steered carefully through the body. In this work, we present a collection of medical criteria that are considered by physicians during an intervention and that can be evaluated automatically enabling a highly objective assessment. Additionally, we trained an autonomous controller with deep reinforcement learning to gently navigate within a 2D simulation of an aortic arch. Among others, the controller reduced the maximum and mean contact force applied to the vessel walls by 43% and 29%, respectively.