Timothy M. Kowalewski

Content and Construct Validation of a Robotic Surgery Curriculum Using an Electromagnetic Instrument Tracker

PURPOSE Rapid adoption of robot-assisted surgery has outpaced our ability to train novice roboticists. Objective metrics are required to adequately assess robotic surgical skills and yet surrogates for proficiency, such as economy of motion and tool path metrics, are not readily accessible directly from the da Vinci® robot system. The trakSTAR™ Tool Tip Tracker is a widely available, cost-effective electromagnetic position sensing mechanism by which objective proficiency metrics can be quantified. We validated a robotic surgery curriculum using the trakSTAR device to objectively capture robotic task proficiency metrics. MATERIALS AND METHODS Through an institutional review board approved study 10 subjects were recruited from 2 surgical experience groups (novice and experienced). All subjects completed 3 technical skills modules, including block transfer, intracorporeal suturing/knot tying (fundamentals of laparoscopic surgery) and ring tower transfer, using the da Vinci robot with the trakSTAR device affixed to the robotic instruments. Recorded objective metrics included task time and path length, which were used to calculate economy of motion. Student t test statistics were performed using STATA®. RESULTS The novice and experienced groups consisted of 5 subjects each. The experienced group outperformed the novice group in all 3 tasks. Experienced surgeons described the simulator platform as useful for training and agreed with incorporating it into a residency curriculum. CONCLUSIONS Robotic surgery curricula can be validated by an off-the-shelf instrument tracking system. This platform allows surgical educators to objectively assess trainees and may provide credentialing offices with a means of objectively assessing any surgical staff member seeking robotic surgery privileges at an institution.

Crowdsourcing to assess surgical skill

What Is the Innovation? Surgical skills impact patient outcomes.1 Our profession needs methods that accurately and objectively evaluate surgical skills.2 These methods must provide timely and meaningful feedback, minimize review time burden, and scale for widespread use. This evaluation hurdle may be overcome by leveraging crowdsourcing to help triage outliers and focus improvement efforts. Crowdsourcing is the process of using large groups of decentralized, independent people providing aggregated feedback.3 Crowdsourcing to assess surgical skill is a method by which surgical technique can be assessed by crowds of reviewers, some of whom are nonmedically trained. Crowdsourcing has enjoyed broad success in health care—discovering protein-folding patterns, assisting disabled patients, locating automatic defibrillators within cities, and annotating electronic medical records.3

Crowd-sourced assessment of technical skills: an adjunct to urology resident surgical simulation training.

Abstract Background: Crowdsourcing is the practice of obtaining services from a large group of people, typically an online community. Validated methods of evaluating surgical video are time-intensive, expensive, and involve participation of multiple expert surgeons. We sought to obtain valid performance scores of urologic trainees and faculty on a dry-laboratory robotic surgery task module by using crowdsourcing through a web-based grading tool called Crowd Sourced Assessment of Technical Skill (CSATS). Methods: IRB approval was granted to test the technical skills grading accuracy of Amazon.com Mechanical Turk™ crowd-workers compared to three expert faculty surgeon graders. The two groups assessed dry-laboratory robotic surgical suturing performances of three urology residents (PGY-2, -4, -5) and two faculty using three performance domains from the validated Global Evaluative Assessment of Robotic Skills assessment tool. Results: After an average of 2 hours 50 minutes, each of the five videos received 50...

Automated Tool Handling for the Trauma Pod Surgical Robot

In order to enable robotic surgery without human assistance, a means must be developed to change tools. As part of the larger Trauma Pod Project, we developed the Tool Rack Subsystem - an automated tool rack capable of holding, accepting, and dispensing up to 14 tools for the da Vinci surgical robot. Borrowing some techniques from industrial automation, we developed a robust system capable of presenting any stored tool in 700ms or less. Tools are positively retained in a sterilizable carousel in a compliant manner designed to accomodate misalignment during tool exchange. RFID equipment is integrated into the system and the tools so that tools can be inventoried and presented by function or serial number instead of rack position. The resulting device has completed testing and integration into the Trauma Pod system and met all its design requirements.

Practical, stretchable smart skin sensors for contact-aware robots in safe and collaborative interactions

Safe, intuitive human-robot interaction requires that robots intelligently interface with their environments, ideally sensing and localizing physical contact across their link surfaces. We introduce a stretchable smart skin sensor that provides this function. Stretchability allows it to conform to arbitrary robotic link surfaces. It senses contact over nearly the entire surface, localizes contact position of a typical finger touch continuously over its entire surface (RMSE = 7.02mm for a 14.7cm×14.7cm area), and provides an estimate of the contact force. Our approach exclusively employs stretchable, flexible materials resulting in skin strains of up to 150%. We exploit novel carbon nanotube elastomers to create a two-dimensional potentiometer surface. Finite element simulations validate a simplified polynomial surface model to enable real-time processing on a basic microcontroller with no supporting electronics. Using only five electrodes, the skin can be scaled up to arbitrary sizes without needing additional electrodes. We designed, implemented, calibrated, and tested a prototype smart skin as a tactile sensor on a custom medical robot for sensing unexpected physical interactions. We experimentally demonstrate its utility in collaborative robotic applications by showing its potential to enable safer, more intuitive human-robot interaction.

Large-Scale Needfinding: Methods of Increasing User-Generated Needs From Large Populations

Understanding user needs and preferences is increasingly recognized as a critical component of early stage product development. The large-scale needfinding methods in this series of studies attempt to overcome shortcomings with existing methods, particularly in environments with limited user access. The three studies evaluated three specific types of stimuli to help users describe higher quantities of needs. Users were trained on need statements and then asked to enter as many need statements and optional background stories as possible. One or more stimulus types were presented, including prompts (a type of thought exercise), shared needs, and shared context images. Topics used were general household areas including cooking, cleaning, and trip planning. The results show that users can articulate a large number of needs unaided, and users consistently increased need quantity after viewing a stimulus. A final study collected 1735 needs statements and 1246 stories from 402 individuals in 24 hr. Shared needs and images significantly increased need quantity over other types. User experience (and not expertise) was a significant factor for increasing quantity, but may not warrant exclusive use of high-experience users in practice.

Exploratory Visualization of Surgical Training Databases for Improving Skill Acquisition

A new visualization system analyzes multidimensional surgical performance databases of information collected via emerging surgical robot and simulator technologies. In particular, it has visualized force, position, rotation, and synchronized video data from 300 bimanual laparoscopic surgery tasks performed by more than 50 surgeons. To explore data, the system uses a multiple-coordinated-views framework. It provides techniques to select and filter multivariate time series data, visualize animated force plots in conjunction with contextual videos, encode multivariate bimanual tool trace data in 3D visualizations, and link visualizations to a database management system via a new generalizable data model. Insights and feedback from an interdisciplinary iterative design process and use case studies support the utility of visualization in this emerging area of data-driven surgical training.

Tissue Identification Through Back End Sensing on da Vinci EndoWrist Surgical Tool

Surgical robots are becoming more common in the operating room. Although surgeons utilize these robots for improved dexterity, scalable movements, and enhanced vision, they lose their sense of tactile and haptic feedback [1]. Okamura demonstrated a negative consequence of this by showing that forces exerted during robotic sutures significantly exceed that of hand sutures [2]. This excess in force can lead to a variety of complications including tissue crushing, which has been shown to be a clinically relevant problem [3]. Sie et al. proposed tissue-aware grasping as a solution for tissue crush injury, which may obviate the need for tactile and haptic feedback altogether [4]. By coupling online tissue identification with tissue-specific thresholds for crush injury, the surgical robot can warn of imminent tissue crushing or potentially prevent it. Sie et al. provided relevant work in this area by validating an approach for online tissue identification within the first 0.3 s of a grasp. This work was done with a modified manual laparoscopic Babcock grasper; this is a specialized instrument not commonly used in surgery. We herein aim to extend the results of Sie et al. to a much more common surgical tool: the da Vinci EndoWrist surgical instrument (Intuitive Surgical, Sunnyvale, CA). We demonstrate that tissue identification is possible using existing robotic tools without additional sensors or modifications to the tool tip, by using only motor torque and position data at the proximal end of the tool.

Quantifying Forces at the Tool-Tissue Interface of a Surgical Laparoscopic Grasper

In laparoscopic minimally invasive surgeries (MIS) surgeons use laparoscopic tools to interface with tissue, performing tissue manipulation without haptic force feedback. Excessive force may cause tissue injury, leading to an increased rate of fatality from perforation, hemorrhage, or the less severe ileus, infection, and adhesion formation [1]. While the actual amount of force exerted at the tool-tissue interface during MIS is critical, current technologies and platforms such as FLS [2], ICSAD [3], and ADEPT [4] provide little or no information on the quantified force value. Prior work by Brown et al. [5], Rosen et al. [6], De [1], and Roan [7] developed tools for measuring tool-tissue interactions on force and deformations with real time feedback on in vivo porcine models. However, the platforms presented force measurement exerted by the surgeons, obtained from sensors located at the proximal end of the tool. The actual amount of force delivered to the tissue was not measured directly but derived using kinematic relations of the tool linkage mechanism. In this work, we develop a portable, general test bed for measuring the force at the proximal end (the force applied by surgeon’s hand at the handle) and directly at the distal end (the force delivered to the tissue by the grasper jaws) of a laparoscopic grasper. We experimentally characterize the mappings between proximally measured and distally applied forces for two different laparoscopic tools: the motorized Mechanical Smart Endoscopic Grasper (MSEG) developed by Roan [7] and the Electronic Data Generation and Evaluation (EDGE) system (Simulab Corporation, Seattle, WA).

Freeing the serial mechanism designer from inverse kinematic solvability constraints

This paper presents a fast numerical solution for the inverse kinematics of a serial manipulator. The method is implemented on the C-arm, a manipulator designed for use in robotic surgery. The inverse kinematics solution provides all possible solutions for any six degree-of-freedom serial manipulator, assuming that the forward kinematics are known and that it is possible to solve for the remaining joint angles if one joint angles value is known. With a fast numerical method and the current levels of computing power, designing a manipulator with closed-form inverse kinematics is no longer necessary. When designing the C-arm, we therefore chose to weigh other factors, such as actuator size and patient safety, more heavily than the ability to find a closed-form inverse kinematics solution.