Recurrent neural networks for generalization towards the vessel geometry in autonomous endovascular guidewire navigation in the aortic arch.

ABSTRACT:

Purpose

Endovascular intervention is the state-of-the-art treatment for common cardiovascular diseases, such as heart attack and stroke. Automation of the procedure may improve the working conditions of physicians and provide high-quality care to patients in remote areas, posing a major impact on overall treatment quality. However, this requires the adaption to individual patient anatomies, which currently poses an unsolved challenge.

Methods

This work investigates an endovascular guidewire controller architecture based on recurrent neural networks. The controller is evaluated in-silico on its ability to adapt to new vessel geometries when navigating through the aortic arch. The controller's generalization capabilities are examined by reducing the number of variations seen d

SUBMITTER: Karstensen L 

PROVIDER: S-EPMC10491528 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature