Face, Content, and Construct Validity of dV-Trainer, a Novel Virtual Reality Simulator for Robotic Surgery

Kenney, Patrick A.; Wszolek, Matthew F.; Gould, Justin J.; Libertino, John A.; Moinzadeh, Alireza

doi:10.1016/j.urology.2008.12.044

Cited by 200 publications

(114 citation statements)

References 5 publications

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“…Although the construct validity of the dV-Trainer has been previously demonstrated, previous studies only included novices and experts, without a group of participants with intermediate level of robotic-assisted laparoscopic surgical expertise. [9][10][11] The advantage of the dVSS over the dV-Trainer is that it uses the same platform in the operating theatre, namely the Si console of the da Vinci robot. This means the trainee acquires skills using the same foot and finger clutch controls, as well as the endowrist manipulations that are used in the operating theatre.…”

Section: Introductionmentioning

confidence: 99%

Validation of the da Vinci Surgical Skill Simulator across three surgical disciplines

Alzahrani

Haddad

Alkhayal

et al. 2013

CUAJ

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Objective: In this paper, we evaluate face, content and construct validity of the da Vinci Surgical Skills Simulator (dVSSS) across 3 surgical disciplines. Methods: In total, 48 participants from urology, gynecology and general surgery participated in the study as novices (0 robotic cases performed), intermediates (1-74) or experts (≥75). Each participant completed 9 tasks (Peg board level 2, match board level 2, needle targeting, ring and rail level 2, dots and needles level 1, suture sponge level 2, energy dissection level 1, ring walk level 3 and tubes). The Mimic Technologies software scored each task from 0 (worst) to 100 (best) using several predetermined metrics. Face and content validity were evaluated by a questionnaire administered after task completion. Wilcoxon test was used to perform pair wise comparisons. Results: The expert group comprised of 6 attending surgeons. The intermediate group included 4 attending surgeons, 3 fellows and 5 residents. The novices included 1 attending surgeon, 1 fellow, 13 residents, 13 medical students and 2 research assistants. The median number of robotic cases performed by experts and intermediates were 250 and 9, respectively. The median overall realistic score (face validity) was 8/10. Experts rated the usefulness of the simulator as a training tool for residents (content validity) as 8.5/10. For construct validity, experts outperformed novices in all 9 tasks (p < 0.05). Intermediates outperformed novices in 7 of 9 tasks (p < 0.05); there were no significant differences in the energy dissection and ring walk tasks. Finally, experts scored significantly better than intermediates in only 3 of 9 tasks (matchboard, dots and needles and energy dissection) (p < 0.05). Conclusions: This study confirms the face, content and construct validities of the dVSSS across urology, gynecology and general surgery. Larger sample size and more complex tasks are needed to further differentiate intermediates from experts. IntroductionThe American College of Surgeons has recommended simulation-based training for surgical trainees to increase proficiency and patient safety in light of recent restrictions to trainee work hours and increased concerns over patient safety.1 The first validated low-fidelity objective training and assessment tool for basic laparoscopic skills is the McGill Inanimate System for Training and Evaluation of Laparoscopic Skills (MISTELS), which has been adapted by the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) for the Fundamentals of Laparoscopic Surgery (FLS) program.2 Currently, FLS certification is required for candidates applying for certification from the American Board of Surgery. Furthermore, construct validity of MISTELS has been established for urology trainees and attending urologists.3 Recently, the American Urological Association's Committee on Laparoscopy, Robotic and New Technology modified the original MISTELS/FLS program into a more urocentric curriculum, the Basic Laparoscopic Urologic Surgery (BLUS) skills curriculu...

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Section: Introductionmentioning

confidence: 99%

Validation of the da Vinci Surgical Skill Simulator across three surgical disciplines

Alzahrani

Haddad

Alkhayal

et al. 2013

CUAJ

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“…Because of variable content, scenarios, and training objectives, it might not be practical to rely solely on traditional statistical analysis. Traditional analysis [7][8][9][10][11][12][13][14] does not distinguish between clinical task skills (e.g., suturing) from the complex human-machine interactions needed to proficiently operate the robotic console (e.g., master workspace adjustment, or camera control). Task time, structured assessment [11][12][13], and errors do not capture all the additional complexity inherent in robotic surgery.…”

Section: Resultsmentioning

confidence: 99%

Automated Support for da Vinci Surgical System

Berkley¹

2011

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Mimic Technologies, Inc. Seattle, WA 98104 PERFORMING ORGANIZATION REPORT NUMBER SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)U.S. Army Medical Research and Materiel Command Fort Detrick, Maryland 21702-5012 SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION / AVAILABILITY STATEMENTApproved for Public Release; Distribution Unlimited SUPPLEMENTARY NOTES ABSTRACTWhile adoption of da Vinci systems has been rapid worldwide, there exists a wide variance in surgical procedure performance impacting care quality, cost and patient safety negatively, due in part to inefficient training practices and limited mechanisms for objectively assessing surgical performance. To address the demand for improved training, Mimic Technologies has developed da Vinci simulators, the dV-Trainer and da Vinci Skills Simulator, in collaboration with Intuitive Surgical, which collect and analyze diverse performance data, and then recommend steps for addressing identified weaknesses. Conversely, Johns Hopkins University's Surgical Assistant Workstation collects and analyzes data from da Vinci during training and surgery. In this Phase I feasibility study, we developed a proof-of-concept working prototype framework capable of providing continuous surgical skills assessment and decision support throughout initial training and thereafter during surgery with the ultimate goal of accelerating a surgeon's acquisition of da Vinci surgical skills. Preliminary analysis of training tasks (anastomosis and peg transfer) across simulation and phantom training laboratories performed by the developed prototype framework highlights the promise of cross-platform data collection and performance measurement across training curriculum, and the opportunity for designing dynamic, customized curricula to a trainee's needs. Developed prototype framework is also a first effort towards cross-platform assessment of robotic surgical performance. This work explored several new concepts including a distributed system for decision support, the role played by instrument and hand pose in planning surgical tasks and metrics assessing this role, distinction of man-machine skills from surgical and task skills, and refinement of simulation training environments to match the corresponding real world training. Preliminary results show that selected performance metrics previously validated in simulation environment continue to hold in training exercises with a real robotic system. The...

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“…Virtual reality surgical simulations could easily be programmed to emulate cases diicult or rare in the real world with high resemblance, hence saving animal and patient models, while signiicantly reducing the surgical training cost [27][28][29]. The VR-based surgical training was reported to be eicient in training new surgeons to robotic surgery [30,31].…”

Section: Surgical Robots Todaymentioning

confidence: 99%

The Next-Generation Surgical Robots

Wang¹,

Liu²,

Peng³

et al. 2018

Surgical Robotics

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The chronicle of surgical robots is short but remarkable. Within 20 years since the regulatory approval of the irst surgical robot, more than 3,000 units were installed worldwide, and more than half a million robotic surgical procedures were carried out in the past year alone. The exceptionally high speeds of market penetration and expansion to new surgical areas had raised technical, clinical, and ethical concerns. However, from a technological perspective, surgical robots today are far from perfect, with a list of improvements expected for the next-generation systems. On the other hand, robotic technologies are lourishing at ever-faster paces. Without the inherent conservation and safety requirements in medicine, general robotic research could be substantially more agile and explorative. As a result, various technical innovations in robotics developed in recent years could potentially be grafted into surgical applications and ignite the next major advancement in robotic surgery. In this article, the current generation of surgical robots is reviewed from a technological point of view, including three of possibly the most debated technical topics in surgical robotics: vision, haptics, and accessibility. Further to that, several emerging robotic technologies are highlighted for their potential applications in next-generation robotic surgery.

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Face, Content, and Construct Validity of dV-Trainer, a Novel Virtual Reality Simulator for Robotic Surgery

Cited by 200 publications

References 5 publications

Validation of the da Vinci Surgical Skill Simulator across three surgical disciplines

Validation of the da Vinci Surgical Skill Simulator across three surgical disciplines

Automated Support for da Vinci Surgical System

The Next-Generation Surgical Robots

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