Mp01-16 Validation of 3d Printed Vua Model Utilizing Automated and Manually Observed Metrics, Featuring Tissue Feedback Measures
Year: 2018
Type: article
Abstract: You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment I1 Apr 2018MP01-16 VALIDATION OF 3D PRINTED VUA MODEL UTILIZING AUTOMATED AND MANUALLY OBSERVED METRICS, FEATURING TISSUE FEEDBACK MEASURES Andrew Hung, Jian Chen, Austin Fullenkamp, Paul Oh, Michael Lin-Brande, Yasser Noureldin, Troy Reihsen, and Robert Sweet Andrew HungAndrew Hung More articles by this author , Jian ChenJian Chen More articles by this author , Austin FullenkampAustin Fullenkamp More articles by this author , Paul OhPaul Oh More articles by this author , Michael Lin-BrandeMichael Lin-Brande More articles by this author , Yasser NoureldinYasser Noureldin More articles by this author , Troy ReihsenTroy Reihsen More articles by this author , and Robert SweetRobert Sweet More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.122AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The vesicourethral anastomosis (VUA) is a critical reconstructive step of the robot-assisted radical prostatectomy. We evaluated validity of the ″UW CREST VUA″ dry lab model derived from 3D printing using questionnaires, manually observed and automated performance metrics (APMs). METHODS Novice (<100 prior cases) and expert (≥100 cases) surgeons completed VUA on silicone models derived from a 3D-printed mold (Figure). Questionnaires assessed the model′s realism and training utility per experts on a Likert scale. APMs (instrument motion-tracking and Endowrist manipulation) were captured from the robot utilizing a novel recorder (dVLogger; Intuitive Surgical). Manually observed: After completion, the VUA was checked for stitch frequency and depth. Maximal tissue deviation during needle driving was captured by a DSLR camera and calculated with pixel-to-mm ratio. APMs were correlated to maximal tissue deviation using Spearman correlation. Kruskal-Wallis test compared performance between groups. RESULTS 3 experts (median: 500 cases (200-2000)) and 4 novices (0 cases) participated. The model was rated 5/5 (4-5) for realism, and 5/5 for training usefulness. MANUALLY-OBSERVED METRICS: Experts completed the anastomosis with less stitches (average 12 (12-13)) than novices (14 (12-17)) (p=0.25). For each bite, experts took 3.6mm from the edge of tissue, compared to 2.7mm by novices (p<0.01). Experts attempted fewer needle drives per stitch (1.0 vs 1.7, p=0.01). APMs: Experts had shorter completion time (p<0.01), faster dominant and non-dominant instruments moving velocities (p≤0.01), and faster wrist rotation and angulation with both instruments (p≤0.05). Experts also had longer camera path length and more camera manipulation (p<0.01). MAXIMAL TISSUE DEVIATION: Experts had less tissue deformation than novices (3.5 vs 4.4 mm, p=0.16). Among all surgeons, tissue deviation was associated with non-dominant instrument moving velocity (r=0.58, p<0.01,) and wrist angulation (r=0.54, p=0.01). CONCLUSIONS The UW CREST VUA model exhibits realism and usefulness as a training tool. Additionally, based on automated and manually observed metrics, we are able to differentiate experts/novices. We also associate tissue deviation during needle driving to specific automated metrics. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e8 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Andrew Hung More articles by this author Jian Chen More articles by this author Austin Fullenkamp More articles by this author Paul Oh More articles by this author Michael Lin-Brande More articles by this author Yasser Noureldin More articles by this author Troy Reihsen More articles by this author Robert Sweet More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
Source: The Journal of Urology
Authors: Andrew Hung, Jian Chen, Austin Fullenkamp, Paul Oh, Michael Lin-Brande, Yasser Noureldin, Troy Reihsen, Robert Sweet
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2795611983