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Virtual interactive suturing for the Fundamentals of Laparoscopic Surgery (FLS).

ABSTRACT: BACKGROUND:Suturing with intracorporeal knot-tying is one of the five tasks of the Fundamentals of Laparoscopic Surgery (FLS), which is a pre-requisite for board certification in general surgery. This task involves placing a short suture through two marks in a penrose drain and then tying a double-throw knot followed by two single-throw knots using two needle graspers operated by both hands. A virtual basic laparoscopic skill trainer (VBLaST©) is being developed to represent the virtual versions of the FLS tasks, including automated, real time performance measurement and feedback. In this paper, we present the development of a VBLaST suturing simulator (VBLaST-SS©). Developing such a simulator involves solving multiple challenges associated with fast collision detection, response and force feedback. METHODS:In this paper, we present a novel projection-intersection based knot detection method, which can identify the validity of different types of knots at haptic update rates. A simple and robust edge-edge based collision detection algorithm is introduced to support interactive knot tying and needle insertion operations. A bimanual hardware interface integrates actual surgical instruments with haptic devices enabling not only interactive rendering of force feedback but also realistic sensation of needle grasping, which realizes an immersive surgical suturing environment. RESULTS:Experiments on performing the FLS intracorporeal suturing task show that the simulator is able to run on a standard personal computer at interactive rates. CONCLUSIONS:VBLaST-SS© is a computer-based interactive virtual simulation system for FLS intracorporeal knot-tying suturing task that can provide real-time objective assessment for the user's performance.

SUBMITTER: Qi D

PROVIDER: S-EPMC5685933 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Virtual interactive suturing for the Fundamentals of Laparoscopic Surgery (FLS).

Qi Di D Panneerselvam Karthikeyan K Ahn Woojin W Arikatla Venkata V Enquobahrie Andinet A De Suvranu S

Journal of biomedical informatics 20170922

<h4>Background</h4>Suturing with intracorporeal knot-tying is one of the five tasks of the Fundamentals of Laparoscopic Surgery (FLS), which is a pre-requisite for board certification in general surgery. This task involves placing a short suture through two marks in a penrose drain and then tying a double-throw knot followed by two single-throw knots using two needle graspers operated by both hands. A virtual basic laparoscopic skill trainer (VBLaST©) is being developed to represent the virtual ...[more]

PMID: 28951209

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Project description:BackgroundThe COVID-19 pandemic challenges our ability to provide surgical education, as our ability to gather and train together has been restricted due to safety concerns. However, the importance of quality surgical education has remained. High-fidelity simulation platforms have been developed that merge virtual reality video streams to allow for remote instruction and collaboration. This study sought to validate the use of a merged virtual reality (MVR) platform for the instruction and assessment of the fundamentals of laparoscopic surgery (FLS) skills.MethodsThis was a prospective randomized controlled non-inferiority study. Thirty participants were randomized between three groups: The standard group received in-person instruction and expert feedback, the experimental group received identical training via the MVR platform, and the control group practiced on their own, but received no feedback. All participants were pre-tested for baseline performance at the beginning of the study. Change in performance was evaluated immediately after training and one month later for retention. Ordinary one-way analysis of variance was used to evaluate the effects of time, group, and time-on-group.ResultsThe pre-test confirmed baseline homogeneity between the groups. MVR was non-inferior to standard in-person training for total FLS times on either the post-test (p = 0.632) or the retention test (p = 0.829). Performance was also identical between MVR and standard training groups for each of the individual FLS tasks. Each group improved significantly in nearly all tasks after practice; however, the standard and MVR training groups both improved significantly more than controls for the ligating loop, extracorporeal suturing, intracorporeal suturing, and total FLS task training but did not reach statistical significance for peg transfer and pattern cut tasks.ConclusionThis randomized, controlled trial has demonstrated the use of an MVR platform as non-inferior to in-person instruction for the FLS program, forming the foundation for future work on remote instruction and collaboration.

Project description:BackgroundMinimally invasive surgeons must acquire complex technical skills while minimizing patient risk, a challenge that is magnified in pediatric surgery. Trainees need realistic practice with frequent detailed feedback, but human grading is tedious and subjective. We aim to validate a novel motion-tracking system and algorithms that automatically evaluate trainee performance of a pediatric laparoscopic suturing task.MethodsSubjects (n = 32) ranging from medical students to fellows performed two trials of intracorporeal suturing in a custom pediatric laparoscopic box trainer after watching a video of ideal performance. The motions of the tools and endoscope were recorded over time using a magnetic sensing system, and both tool grip angles were recorded using handle-mounted flex sensors. An expert rated the 63 trial videos on five domains from the Objective Structured Assessment of Technical Skill (OSATS), yielding summed scores from 5 to 20. Motion data from each trial were processed to calculate 280 features. We used regularized least squares regression to identify the most predictive features from different subsets of the motion data and then built six regression tree models that predict summed OSATS score. Model accuracy was evaluated via leave-one-subject-out cross-validation.ResultsThe model that used all sensor data streams performed best, achieving 71% accuracy at predicting summed scores within 2 points, 89% accuracy within 4, and a correlation of 0.85 with human ratings. 59% of the rounded average OSATS score predictions were perfect, and 100% were within 1 point. This model employed 87 features, including none based on completion time, 77 from tool tip motion, 3 from tool tip visibility, and 7 from grip angle.ConclusionsOur novel hardware and software automatically rated previously unseen trials with summed OSATS scores that closely match human expert ratings. Such a system facilitates more feedback-intensive surgical training and may yield insights into the fundamental components of surgical skill.

Project description:BackgroundCompared to conventional laparoscopy, robot assisted surgery is expected to have most potential in difficult areas and demanding technical skills like minimally invasive suturing. This study was performed to identify the differences in the learning curves of laparoscopic versus robot assisted suturing.MethodNovice participants performed three suturing tasks on the EoSim laparoscopic augmented reality simulator or the RobotiX robot assisted virtual reality simulator. Each participant performed an intracorporeal suturing task, a tilted plane needle transfer task and an anastomosis needle transfer task. To complete the learning curve, all tasks were repeated up to twenty repetitions or until a time plateau was reached. Clinically relevant and comparable parameters regarding time, movements and safety were recorded. Intracorporeal suturing time and cumulative sum analysis was used to compare the learning curves and phases.ResultsSeventeen participants completed the learning curve laparoscopically and 30 robot assisted. Median first knot suturing time was 611 s (s) for laparoscopic versus 251 s for robot assisted (p < 0.001), and this was 324 s versus 165 (sixth knot, p < 0.001) and 257 s and 149 s (eleventh knot, p < 0.001) respectively on base of the found learning phases. The percentage of 'adequate surgical knots' was higher in the laparoscopic than in the robot assisted group. First knot: 71% versus 60%, sixth knot: 100% versus 83%, and eleventh knot: 100% versus 73%. When assessing the 'instrument out of view' parameter, the robot assisted group scored a median of 0% after repetition four. In the laparoscopic group, the instrument out of view increased from 3.1 to 3.9% (left) and from 3.0 to 4.1% (right) between the first and eleventh knot (p > 0.05).ConclusionThe learning curve of minimally invasive suturing shows a shorter task time curve using robotic assistance compared to the laparoscopic curve. However, laparoscopic outcomes show good end results with rapid outcome improvement.

Dataset Information

Virtual interactive suturing for the Fundamentals of Laparoscopic Surgery (FLS).

Publications

Virtual interactive suturing for the Fundamentals of Laparoscopic Surgery (FLS).

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