Journal of Medical Robotics Research

Lung cancer is the most deadly form of cancer in part because of the challenges associated with accessing nodules for diagnosis and therapy. Transoral access is preferred to percutaneous access since it has a lower risk of lung collapse, yet many sites are currently unreachable transorally due to limitations with current bronchoscopic instruments. Toward this end, we present a new robotic system for image-guided trans-bronchoscopic lung access. The system uses a bronchoscope to navigate in the airway and bronchial tubes to a site near the desired target, a concentric tube robot to move through the bronchial wall and aim at the target, and a bevel-tip steerable needle with magnetic tracking to maneuver through lung tissue to the target under closed-loop control. In this work, we illustrate the workflow of our system and show accurate targeting in phantom experiments. Ex vivo porcine lung experiments show that our steerable needle can be tuned to achieve appreciable curvature in lung tissue. Lastly, we present targeting results with our system using two scenarios based on patient cases. In these experiments, phantoms were created from patient-specific computed tomography information and our system was used to target the locations of suspicious nodules, illustrating the ability of our system to reach sites that are traditionally inaccessible transorally.

Continuum manipulators enable minimally-invasive surgery on the beating heart, but the challenges involved in manually controlling the manipulator’s tip position and contact force with the tissue result in failed procedures and complications. The objective of this work is to achieve autonomous robotic control of a continuum manipulator’s position and force in a beating heart model. We present a model-less hybrid control approach that regulates the tip position/force of manipulators with unknown kinematics/mechanics, under unknown constraints along the manipulator’s body. The algorithms estimate the Jacobian in the presence of heartbeat disturbances and sensor noise in real time, enabling closed-loop control. Using this model-less control approach, a robotic catheter autonomously traced clinically relevant paths on a simulated beating heart environment while regulating contact force. A gating procedure is used to tighten the treatment margins and improve precision. Experimental results demonstrate the capabilities of the robot ($1.4\pm 1.1$mm–$1.9\pm 1.4$mm tracking error) while user demonstrations show the difficulty of manually performing the same task ($2.6\pm 2.0$mm–$4.3\pm 3.9$mm tracking error). This new, robotically-enabled contiguous ablation method could reduce ablation path discontinuities, improve consistency of treatment, and therefore improve clinical outcomes.

Automatic detection of the current task load of a surgeon in the theatre in real time could provide helpful information, to be used in supportive systems. For example, such information may enable the system to automatically support the surgeon when critical or stressful periods are detected, or to communicate to others when a surgeon is engaged in a complex maneuver and should not be disturbed. Passive brain–computer interfaces (BCI) infer changes in cognitive and affective state by monitoring and interpreting ongoing brain activity recorded via an electroencephalogram. The resulting information can then be used to automatically adapt a technological system to the human user. So far, passive BCI have mostly been investigated in laboratory settings, even though they are intended to be applied in real-world settings. In this study, a passive BCI was used to assess changes in task load of skilled surgeons performing both simple and complex surgical training tasks. Results indicate that the introduced methodology can reliably and continuously detect changes in task load in this realistic environment.

Intracerebral hemorrhage evacuation (ICH) using a tubular aspiration robot promises benefits over conventional approaches to release the pressure of an hemorrhage within the brain. The blood of the hemorrhage is evacuated through preplanned, coordinated motion of a flexible, curved, concentric tube that aspirates from within the hemorrhage. To achieve maximum decompression, the curvature of the inner aspirator tube has to be selected such that its workspace covers the hemorrhage. As the use of multiple aspiration tubes sequentially is advisable, one can perform an exhaustive search over all possible aspiration tube shapes as has been previously proposed in the literature. In this paper, we introduce a new optimization algorithm which is computationally more efficient and thus allows for quick optimization during surgery. To demonstrate its performance and compare it to the previously proposed exhaustive search algorithm, we present experimental evaluation results on 175 simulated patient trials.