DeepNav: Joint View Learning for Direct Optimal Path Perception in Cochlear Surgical Platform Navigation

Although much research has been conducted in the field of automated cochlear implant navigation, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as identifying the optimal navigation zone (OPZ) in the cochlear. In this paper, a 2.5D joint-view convolutional neural network (2.5D CNN) is proposed and evaluated for the identification of the OPZ in the cochlear segments. The proposed network consists of 2 complementary sagittal and bird-view (or top view) networks for the 3D OPZ recognition, each utilizing a ResNet-8 architecture consisting of 5 convolutional layers with rectified nonlinearity unit (ReLU) activations, followed by average pooling with size equal to the size of the final feature maps. The last fully connected layer of each network has 4 indicators, equivalent to the classes considered: the distance to the adjacent left and right walls, collision probability and heading angle. To demonstrate this, the 2.5D CNN was trained using a parametric data generation model, and then evaluated using anatomically constructed cochlea models from the micro-CT images of different cases. Prediction of the indicators demonstrates the effectiveness of the 2.5D CNN, for example the heading angle has less than 1° error with computation delays of less that <1 milliseconds

Robots and tools for remodeling bone

The field of robotic surgery has progressed from small teams of researchers repurposing industrial robots, to a competitive and highly innovative subsection of the medical device industry. Surgical robots allow surgeons to perform tasks with greater ease, accuracy, or safety, and fall under one of four levels of autonomy; active, semi-active, passive, and remote manipulator. The increased accuracy afforded by surgical robots has allowed for cementless hip arthroplasty, improved postoperative alignment following knee arthroplasty, and reduced duration of intraoperative fluoroscopy among other benefits. Cutting of bone has historically used tools such as hand saws and drills, with other elaborate cutting tools now used routinely to remodel bone. Improvements in cutting accuracy and additional options for safety and monitoring during surgery give robotic surgeries some advantages over conventional techniques. This article aims to provide an overview of current robots and tools with a common target tissue of bone, proposes a new process for defining the level of autonomy for a surgical robot, and examines future directions in robotic surgery

Electrical Impedance to Assess Facial Nerve Proximity during Robotic Cochlear Implantation

Reported studies pertaining to needle guidance suggest that tissue impedance available from neuromonitoring systems can be used to discriminate nerve tissue proximity. In this pilot study, the existence of a relationship between intraoperative electrical impedance and tissue density, estimated from computer tomography (CT) images, is evaluated in the mastoid bone of in- vivo sheep. In five subjects, nine trajectories were drilled using an image-guided surgical robot. Per trajectory, five measurement points near the facial nerve were accessed and electrical impedance was measured (≤ 1 KHz) using a multipolar electrode probe. Micro-CT was used postoperatively to measure the distances from the drilled trajectories to the facial nerve. Tissue density was determined from co-registered preoperative CT images and, following sensitivity field modelling of the measuring tip, tissue resistivity was calculated. The relationship between impedance and density was determined for 29 trajectories passing or intersecting the facial nerve. A monotonic decrease in impedance magnitude was observed in all trajectories with a drill axis intersecting the facial nerve. Mean tissue densities intersecting with the facial nerve (971-1161 HU) were different (p < 0.01) from those along safe trajectories passing the nerve (1194-1449 HU). However, mean resistivity values of trajectories intersecting the facial nerve (14-24 Ωm) were similar to those of safe passing trajectories (17-23 Ωm). The determined relationship between tissue density and electrical impedance during neuromonitoring of the facial nerve suggests that impedance spectroscopy may be used to increase the accuracy of tissue discrimination, and ultimately improve nerve safety distance assessment in the future