Colonoscopy demands multi-finger coordinated motion to achieve safe navigation. As a result, training for colonoscopists is challenging and skill assessment currently relies on subjective scoring by expert proctors. There is a need to provide tools for skill assessment and aid with training new interventionalists. This paper presents a new concept of an in-hand robotic mediation device that can be used for both skill assessment and training. The robotic device can be used to infer the kinematic motion as well as the power input of a user - both of which are proposed to be used for skill assessment and subtask skill classification. Preliminary results collected expert and novice users performing colonoscopy navigation are used to demonstrate this device as a skill assessment tool. A machine-learning model (classification and regression trees) is used for subtask classification of skill and evaluating the most important classification features. A user study demonstrates the effectiveness of this in hand haptic training and assessment tool. We believe that, in the future, this device will enable accelerated skill assessment and training and possible semi-automation of difficult maneuvers.

Injections into specific retinal layers of the eye present a serious challenge to surgeons in terms of accuracy and perception. The emergence of new gene therapies further emphasizes the need for effective tools for localized drug delivery. Unlike the dominant approach of delivering drugs via a transvitreal intraocular pathway, this paper demonstrates the feasibility of delivering injections into the space between the choroid and the retina using an external approach. The design of a cooperative robotic system for enabling robot-assisted extraocular subretinal injections is presented. The system uses a distal micromanipulator that can serve as a hand-held tool for OCT-aided injection or attach to a six degree of freedom (DOF) serial robot arm for cooperative manipulation. The kinematics and control of the robot for constrained cooperative control motions to enable safe needle injection is presented and experimentally evaluated. These results suggest that the proposed external drug delivery approach is feasible, thereby enabling the advantages of preserving the integrity of the retina and omitting the necessity for vitrectomy.

Optical Coherence Elastography (OCE) is a method that discerns local tissue stiffness using optical information. This method has recently been explored for laryngeal cancer tumor margin detection but has not been widely deployed clinically. Part of the challenge hindering such clinical deployment is the need for controlled high-precision mechanical probing of the tissue. This paper explores the concept of robot-assisted optical coherence elastography(OCE) and presents a preliminary system integration used to demonstrate the approach for stiffness mapping and discerning tumor margins. The approach is demonstrated on a custom Cartesian stage robot, and a custom-built OCE system comprised of an 830 nm broad-band laser with a vector-analysis method for phase gradient estimation and strain imaging. The paper illustrates one of the advantages of robot-controlled probing in terms of increasing the accuracy of the OCE system in a large range of displacement and strain. By leveraging motion information from the robot, online re-calibration of the OCE strain map may be achieved, thereby reducing OCE errors. After calibration, it is shown that the error in estimating the local Young's modulus is 0.485% in the silicon phantom and 0.531% in the agar phantom. These results suggest that future integration of optical coherence tomography(OCT) in clinically deployable robots may offer advantages in enabling local stiffness map estimation using OCE.

This article considers a combination of actuation tendons and measurement strings to achieve accurate shape sensing and direct kinematics of continuum robots. Assuming general string routing, a methodical Lie group formulation for the shape sensing of these robots is presented. The shape kinematics is expressed using arc-length-dependent curvature distributions parameterized by modal functions, and the Magnus expansion for Lie group integration is used to express the shape as a product of exponentials. The tendon and string length kinematic constraints are solved for the modal coefficients and the configuration space and body Jacobian are derived. The noise amplification index for the shape reconstruction problem is defined and used for optimizing the string/tendon routing paths, and a planar simulation study shows the minimal number of strings/tendons needed for accurate shape reconstruction. A torsionally stiff continuum segment is used for experimental evaluation, demonstrating mean (maximal) end-effector absolute position error of less than 2% (5%) of total length. Finally, a simulation study of a torsionally compliant segment demonstrates the approach for general deflections and string routings. We believe that the methods of this article can benefit the design process, sensing, and control of continuum and soft robots.