Key result
A learning-based kinematic control framework for soft tendon-driven catheters achieved trajectory tracking errors of 0.49 ± 0.32 mm and 0.62 ± 0.36 mm in slow and fast trajectories, respectively.
Why the study?
The study was conducted to propose and validate a control framework with level-2 autonomy (task autonomy) for flexible ablation catheters.
A novel learning-based control framework for robotic ablation catheters demonstrated submillimeter accuracy in trajectory tracking and target reaching in a benchtop model.
May guide preclinical optimization of soft robotic catheters; leaves open clinical translation and safety validation.
The goal of this study was to propose and validate a control framework with level-2 autonomy (task autonomy) for the control of flexible ablation catheters. To this end, a kinematic model for the flexible portion of typical ablation catheters was developed and a 40-mm-long spring-loaded flexible catheter was fabricated. The feasible space of the catheter was obtained experimentally. Furthermore, a robotic catheter intervention system was prototyped for controlling the length of the catheter tendons. The proposed control framework used a support vector machine classifier to determine the tendons to be driven, and a fully connected neural network regressor to determine the length of the tendons. The classifier and regressors were trained with the data from the feasible space. The control system was implemented in parallel at user-interface and firmware and exhibited a 0.4-s lag in following the input. The validation studies were four trajectory tracking and four target reaching experiments. The system was capable of tracking trajectories with an error of 0.49 ± 0.32 and 0.62 ± 0.36 mm in slow and fast trajectories, respectively. Also, it exhibited submillimeter accuracy in reaching three preplanned targets and ruling out one nonfeasible target autonomously. The results showed improved accuracy and repeatability of the position control compared with the recent literature. The proposed learning-based approach could be used in enabling task autonomy for catheter-based ablation therapies.
No takes yet. Share an insight, caveat, or question.
Jolaei et al. (2020) studied this question. Learning-based kinematic control framework for robotic cardiac ablation catheters was evaluated on Trajectory tracking error and target reaching accuracy. A learning-based kinematic control framework for soft tendon-driven catheters achieved trajectory tracking errors of 0.49 ± 0.32 mm and 0.62 ± 0.36 mm in slow and fast trajectories, respectively.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: