Investigating haptic distance-to-break using linear and nonlinear materials in a simulated minimally invasive surgery task.

Accurate detection of mediated haptic information in minimally invasive surgery (MIS) is critical for applying appropriate force magnitudes onto soft tissue with the aim of minimising tissue trauma. Force perception in MIS is a dynamic process, with surgeons' administration of force into tissue revealing information about the remote surgical site which further informs the surgeons' haptic interactions. The relationship between applied force and material deformation rate provides biomechanical information specifying the deformation distance remaining until a tissue will fail: which is termed distance-to-break (DTB). The current study demonstrates that observers can detect DTB while deforming simulated tissues and stop before reaching the tissues' failure points. The design of training simulators, control devices and automated robotic systems for applications outside of MIS is discussed. Practitioner Summary: In MIS, haptic information is critical for applying appropriate forces onto soft tissue to minimise tissue trauma. Observers used force information to detect how far they could deform a virtual tissue before it would break. The design of training simulators, control devices and automated robotic systems is discussed.

Assessment of Open Surgery Suturing Skill: Image-based Metrics Using Computer Vision.

OBJECTIVE: This paper presents a computer vision algorithm for extraction of image-based metrics for suturing skill assessment and the corresponding results from an experimental study of resident and attending surgeons. DESIGN: A suturing simulator that adapts the radial suturing task from the Fundamentals of Vascular Surgery (FVS) skills assessment is used to collect data. The simulator includes a camera positioned under the suturing membrane, which records needle and thread movement during the suturing task. A computer vision algorithm processes the video data and extracts objective metrics inspired by expert surgeons' recommended best practice, to "follow the curvature of the needle." PARTICIPANTS AND RESULTS: Experimental data from a study involving subjects with various levels of suturing expertise (attending surgeons and surgery residents) are presented. Analysis shows that attendings and residents had statistically different performance on 6 of 9 image-based metrics, including the four new metrics introduced in this paper: Needle Tip Path Length, Needle Swept Area, Needle Tip Area and Needle Sway Length. CONCLUSION AND SIGNIFICANCE: These image-based process metrics may be represented graphically in a manner conducive to training. The results demonstrate the potential of image-based metrics for assessment and training of suturing skill in open surgery.

Nonlinear Adaptive Optimal Controller Design for Anti-Angiogenic Tumor Treatment.

Angiogenesis is an important process in tumor growth as it represents the regime when the tumor recruits blood vessels from the surrounding tissue to support further tumor growth. Anti-angiogenic treatments aim to shrink the tumor by interrupting the vascularization of the tumor; however, the anti-angiogenic agents are costly and the tumor response to these agents is nonlinear. Simple dosing schemes, e.g., a constant dose, may yield higher cost or lower efficacy than an approach that considers the tumor system dynamics. Hence, in this study, the administration of anti-angiogenic treatment is considered as a nonlinear control problem. The main aim of the controller design is to optimize the anti-angiogenic tumor therapy, specifically, to minimize the tumor volume and drug dose. Toward this aim, two nonlinear optimal controllers are presented. The first controller ensures exponential tracking of a desired, optimal tumor volume profile under the assumption that all parameters in the system model are known. The second controller, on the other hand, assumes all the parameters are unknown and provides asymptotic tracking. Both controllers take pharmacokinetics and pharmacodynamics into account, as well as the carrying capacity of the vascular network. Lyapunov based arguments are used to design the controllers, using stability arguments, and numerical simulation results are presented to demonstrate the effectiveness of the proposed method.

Assessment of open surgery suturing skill: Simulator platform, force-based, and motion-based metrics.

Objective: This paper focuses on simulator-based assessment of open surgery suturing skill. We introduce a new surgical simulator designed to collect synchronized force, motion, video and touch data during a radial suturing task adapted from the Fundamentals of Vascular Surgery (FVS) skill assessment. The synchronized data is analyzed to extract objective metrics for suturing skill assessment. Methods: The simulator has a camera positioned underneath the suturing membrane, enabling visual tracking of the needle during suturing. Needle tracking data enables extraction of meaningful metrics related to both the process and the product of the suturing task. To better simulate surgical conditions, the height of the system and the depth of the membrane are both adjustable. Metrics for assessment of suturing skill based on force/torque, motion, and physical contact are presented. Experimental data are presented from a study comparing attending surgeons and surgery residents. Results: Analysis shows force metrics (absolute maximum force/torque in z-direction), motion metrics (yaw, pitch, roll), physical contact metric, and image-enabled force metrics (orthogonal and tangential forces) are found to be statistically significant in differentiating suturing skill between attendings and residents. Conclusion and significance: The results suggest that this simulator and accompanying metrics could serve as a useful tool for assessing and teaching open surgery suturing skill.

Surgical Suturing with Depth Constraints: Image-based Metrics to Assess Skill.

Suturing is one of the most fundamental surgical skills, requiring careful and systematic instruction for skilled performance. In this paper, we evaluate the performance of attending surgeons and surgical residents on an open surgery suturing task to examine if the introduction of different depth levels affects their performance. A vision algorithm is used to extract metrics meaningful in the assessment of suturing skill. As subjects perform a suturing task on the platform, our vision algorithm computes metrics identified to be potentially useful in assessing suturing skill: distances from optimal entry and optimal exit points, stitch length, stitch time, idle time, needle swept area, needle tip trace distance, needle tip area, and needle sway length. Preliminary experimental data from a study with 5 attending surgeons and 7 surgical residents are presented. Results demonstrate that the metrics of distance from optimal exit points, idle time, needle swept area, needle tip trace distance, needle tip area, and needle sway length are useful in quantifying the effect of depth constraints on suturing performance.

Learning to perceive haptic distance-to-break in the presence of friction.

Two experiments employed attunement and calibration training to investigate whether observers are able to identify material break points in compliant materials through haptic force application. The task required participants to attune to a recently identified haptic invariant, distance-to-break (DTB), rather than haptic stimulation not related to the invariant, including friction. In the first experiment participants probed simulated force-displacement relationships (materials) under 3 levels of friction with the aim of pushing as far as possible into the materials without breaking them. In a second experiment a different set of participants pulled on the materials. Results revealed that participants are sensitive to DTB for both pushing and pulling, even in the presence of varying levels of friction, and this sensitivity can be improved through training. The results suggest that the simultaneous presence of friction may assist participants in perceiving DTB. Potential applications include the development of haptic training programs for minimally invasive (laparoscopic) surgery to reduce accidental tissue damage. (PsycINFO Database Record