Papers for
robotic surgery system developers
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Catheter tip position estimated more accurately without shaft sensors
Shaft-Configuration-Adaptive Catheter Tip Position Estimation via Motor-History Conditioned Residual Learning
Abstract: Tendon-driven continuum manipulators are widely used in medical applications, where accurate tip-position estimation is essential for precise navigation and instrument positioning. However, patient anatomy and procedural setup impose task-dependent unknown shaft configurations, while friction, slack, and compliance introduce hysteresis, making tip estimation challenging. This paper presents a motor-history-conditioned gated recurrent unit (GRU) residual estimator for three-dimensional catheter tip estimation without direct shaft-configuration sensing. First, an initial multidirectional sweep strategy is applied to calibrate a geometric catheter model backbone, and encode the motor-angle and drive-torque response into a shaft-configuration context vector. During subsequent motion, the context conditions a GRU that predicts a task-space residual correcting this backbone, relying on motor measurements alone. The context remains fixed for the current shaft configuration, while the recurrent state captures the evolving actuation history. Across four disposable intra-cardiac echocardiography catheters and 16 bent shaft configurations, the method achieves 3.3mm open-loop tip RMSE, a 59% reduction relative to the constant-curvature baseline.
Earable tooth-click control helps neurosurgeons operate hands-free
NeuroClick: Preserving Surgeon Autonomy through Hands-Free Earable Tooth-Click Control in Neurosurgery
Abstract: Neurosurgeons frequently interact with operating room (OR) technologies while sterility and occupied hands constrain control. We introduce earables as a direct, hands-free control platform for neurosurgery using tooth-click input. Formative OR observations and interviews with 10 domain experts grounded the design. Using OpenEarable 2.0 data from 12 participants, we developed a real-time recognition pipeline whose classifier achieved a median macro F1-score of 98.6% under leave-one-subject-out cross-validation. We evaluated the technique with 20 neurosurgeons during a simulated resection task in a neurosurgical OR. Participants reported few focus shifts and rated Earable favorably for workflow integration and perceived safety. Autonomous microscope control was rated significantly higher with Earable than Delegation, whereas Delegation enabled faster task completion under continuous assistant availability. Workload, usability, and task errors showed no significant differences. Preferences depended on training, reliability, context, and assistant availability. Earables thus add a direct, hands-free option for controlling selected functions alongside established workflows.