In retinal surgery, surgeons face difficulties such as indirect visualization of surgical targets, physiological tremor and lack of tactile feedback, which increase the risk of retinal damage caused by incorrect surgical gestures. In this context, intra-ocular proximity sensing has the potential to overcome current technical limitations and increase surgical safety. In this paper we present a system for detecting unintentional collisions between surgical tools and the retina using the visual feedback provided by the opthalmic stereo microscope. Using stereo images, proximity between surgical tools and the retinal surface can be detected when their relative stereo disparity is small. For this purpose, we developed a system comprised of two modules. The first is a module for tracking the surgical tool position on both stereo images. The second is a disparity tracking module for estimating a stereo disparity map of the retinal surface. Both modules were specially tailored for coping with the challenging visualization conditions in retinal surgery. The potential clinical value of the proposed method is demonstrated by extensive testing using a silicon phantom eye and recorded rabbit in vivo data.
Many robotics tasks require a robot to share the same workspace with humans. In such settings, it is important that the robot performs in such a way that does not cause distress to humans in the workspace. In this paper, we address the problem of designing robot controllers which minimize the stress caused by the robot while performing a given task. We present a novel, data-driven algorithm which computes human-friendly trajectories. The algorithm utilizes biofeedback measurements and combines a set of geometric controllers to achieve human friendliness. We evaluate the comfort level of the human using a Galvanic Skin Response (GSR) sensor. We present results from a human tracking task, in which the robot is required to stay within a specified distance without causing high stress values.
Our pilot study suggests that QE can be used to generate precise 3D reconstructions of airways. This technique is atraumatic, does not require ionizing radiation, and integrates easily into standard airway assessment protocols. We conjecture that this technology will be useful for staging airway disease and assessing surgical outcomes.
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