From Patient-specific Digital Twin to Real-worldPhantom: Autonomous Right Heart Catheterization

Right heart catheterization (RHC) is a critical procedure for diagnosing and managing cardiovascular diseases (CVDs) such as heart failure, congenital heart disease, pulmonary edema, and pulmonary hypertension. However, currently prevalent manual RHC procedures requires continuous communication of clinicians between the main control room and the operating room, leading to navigation inaccuracies and increased physical workload for clinicians during prolonged procedure. To overcome these challenges, this paper introduces a robotic system that enables autonomous RHC (Auto-RHC) by transferring a catheter decision-making model from patient-specific digital twins to real-world robotic intervention using deep learning (DL) algorithms. By creating a patient-specific (PS) digital twin using the Simulation Open Framework Architecture (SOFA) and conducting virtual RHC interventions, images capturing the catheter balloon’s position and aligned behavioral datasets were collected and utilized as input for a convolutional neural network (CNN) architecture. The trained catheter decision-making model derived from the digital twin was then transferred to real-world implementations of robot-assisted Auto-RHC. Experimental results validated the performance of the digital twin and demonstrated that the real-world robotic Auto-RHC achieved a high success rate across both static (≥ 96%) and dynamic heartbeat (≥ 94%) patient-specific cardiac phantoms. Furthermore, AutoRHC enhanced navigation consistency by ≥ 34.63% compared to expert manual operation.