Transcatheter tricuspid valve replacement (TTVR) has emerged as a promising minimally invasive procedure for treating severe tricuspid regurgitation (TR). However, accurate catheter delivery remains a significant challenge, primarily due to the reliance on 2D vision feedback, complex catheter kinematics, camera-to-robot pose calibration, which are difficult to generalize across patients. To address these issues, this paper presents a model-free robotic catheter delivery strategy for TTVR using Data-Enabled Predictive Control (DeePC). This approach leverages data-driven control to optimize catheter positioning without the need for prior knowledge of the system’s dynamics, eliminating the need for complex kinematic models or camera calibration. The proposed method incorporates environmental constraints to ensure the safety of the procedure, delivering the catheter to the desired location with high accuracy across varying catheters and camera poses. Experimental results demonstrate the effectiveness and versatility of the approach, suggesting its potential for broader applications in robotic-assisted surgeries. This work presents a new perspective for vision based robotic TTVR, as well as other clinical interventions involving robotic catheter control.

Severe chronic pulmonary endarterectomy needs a dissector to delicately remove proliferative intima located in the depth of the pulmonary artery. This work proposed a novel endoscopic robotized steerable dissector for this surgery, enabling easier access to curved deep artery branches. The handheld surgical dissector also provides suction and visualization for surgeons to enhance effectiveness. The steerable section is a cable-driven hinged structure, and through an antagonistic mechanism regulating the cable tension, the overall stiffness is adjusted to adapt various surroundings. The mapping between actuation space and shape configuration and tip force estimation model are respectively established for further closed-loop control scheme, achieving adaptive positioning and safe surgery. Experiments first demonstrate the feasibility of the proposed models and ex vitro trials validated the usage and effectiveness of the robotized dissector.

Ultrasound (US) imaging of the carotid artery (CA) is a non-invasive diagnostic tool widely used in the medical field to assess the condition of the carotid artery, thereby predicting the risk of cardiovascular and cerebrovascular diseases. However, implementing this method in primary healthcare can be challenging due to the requirement for professionally trained sonographers. With the adoption of US robotic devices, the probe pose can be acquired while scanning, offering the possibility for 3D reconstruction and providing analyses that are not dependent on operator experience. This article introduces a method to semi-automatically acquire serialized US images of the common carotid artery (CCA). The method involves a specially designed robotic device built with a 6-RSU parallel mechanism, which is controlled according to robot pose, force sensor data and synchronous US images. To validate the images acquired, a method is proposed to segment the intima-media of CCA and calculate the intima-media thickness (IMT), which is a key indicator for cerebrovascular events prediction. After that, we propose an algorithm to reconstruct CCA into 3D voxel data with patient movement and cardiac cycle compensated, and a longitudinal view US image of CCA can be resliced from the voxel. The methods are tested on human subjects and the results indicate that the system and workflow can provide both quantitative and qualitative information of CCA for further diagnosis.