Quality-dependent Deep Learning for Safe Autonomous Guidewire Navigation

Ritter J, Karstensen L, Langejürgen J, Hatzl J, Mathis-Ullrich F, Uhl C (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Current Directions in Biomedical Engineering De Gruyter

Book Volume: 8

Pages Range: 21-24

Journal Issue: 1

DOI: 10.1515/cdbme-2022-0006

Abstract

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.

Authors with CRIS profile

Lennart Karstensen
Franziska Mathis-Ullrich

Involved external institutions

Fraunhofer-Institut für Produktionstechnik und Automatisierung (IPA) DE Germany (DE) Universitätsklinikum Heidelberg DE Germany (DE) Karlsruhe Institute of Technology (KIT) DE Germany (DE)

How to cite

APA:

Ritter, J., Karstensen, L., Langejürgen, J., Hatzl, J., Mathis-Ullrich, F., & Uhl, C. (2022). Quality-dependent Deep Learning for Safe Autonomous Guidewire Navigation. Current Directions in Biomedical Engineering, 8(1), 21-24. https://doi.org/10.1515/cdbme-2022-0006

MLA:

Ritter, Jacqueline, et al. "Quality-dependent Deep Learning for Safe Autonomous Guidewire Navigation." Current Directions in Biomedical Engineering 8.1 (2022): 21-24.

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