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• W3022956103 abstract "You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment I (MP34)1 Apr 2020MP34-11 DISTINGUISHING SURGICAL EXPERTISE USING MACHINE LEARNING AND AUTOMATED PERFORMANCE METRICS DURING SUB-STITCHES OF VESICO-URETHRAL ANASTOMOSIS Siqi Liang, Jessica Nguyen*, Jian Chen, Erik Vanstrum, Samuel Mingo, Yan Liu, and Andrew Hung Siqi LiangSiqi Liang More articles by this author , Jessica Nguyen*Jessica Nguyen* More articles by this author , Jian ChenJian Chen More articles by this author , Erik VanstrumErik Vanstrum More articles by this author , Samuel MingoSamuel Mingo More articles by this author , Yan LiuYan Liu More articles by this author , and Andrew HungAndrew Hung More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000878.011AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Automated performance metrics (APMs) objectively measure surgeon performance during a robot-assisted radical prostatectomy (RARP). Machine learning (ML) has shown that APMs, especially during the vesico-urethral anastomosis (VUA) of the RARP, are predictive of long-term outcomes such as continence recovery time. This study focuses on APMs during the VUA, on stitch versus sub-stitch levels, to distinguish surgeon experience. METHODS: During the VUA, APMs, recorded by a systems data recorder (Intuitive Surgical), were reported for each overall stitch (Ctotal) and its individual components: needle handling/targeting (C1), needle driving (C2), and suture cinching (C3) (Figure 1A). These metrics were organized into three datasets (GlobalSet [whole stitch], RowSet [independent sub-stitches], and ColumnSet [associated sub-stitches] (Figure 1B) and applied to three ML models (AdaBoost, Gradient Boosting, and Random Forest) in order to solve two classifications tasks: experts (≥ 100 cases) vs. novices (<100 cases); and ordinary-experts (OE; ≥100 but < 2000 cases) vs. super-experts (SE; ≥ 2000 cases). Classification accuracy was determined using analysis of variance (ANOVA). Input features were evaluated for the stability of their importance to each classification task through a Jaccard index. RESULTS: From 68 VUAs, we analyzed 1,570 stitches broken down into 4,708 sub-stitches. ColumnSet, where sub-stitch APMs were provided as related to its sub-components, consistently produced the highest accuracy across all ML models (p<0.003). For both classification tasks, AdaBoost trained with ColumnSet best distinguished experts (n=8; median: 855 cases) vs. novices (n=9; median: 18 cases) and OE (n=5; median 168) vs. SE (n=3; median: 2000 cases) at an accuracy of 0.774 and 0.844, respectively. Stable feature importance scores highlighted Endowrist® articulation and needle handling/targeting (C1) APMs as most important for classification. CONCLUSIONS: Surgeon performance measured by APMs on a granular sub-stitch level more accurately distinguishes expertise when compared to summary APMs over whole stitches. Wrist articulation and needle handling/targeting APMs were the most important features for accurate experience classification. Source of Funding: This study was funded in part by an Intuitive Surgical Clinical Grant; Intuitive Surgical provided the systems data recorder. Research reported in this publication was supported in part by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number K23EB026493. © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e507-e507 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Siqi Liang More articles by this author Jessica Nguyen* More articles by this author Jian Chen More articles by this author Erik Vanstrum More articles by this author Samuel Mingo More articles by this author Yan Liu More articles by this author Andrew Hung More articles by this author Expand All Advertisement PDF downloadLoading ..." @default.
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• W3022956103 title "MP34-11 DISTINGUISHING SURGICAL EXPERTISE USING MACHINE LEARNING AND AUTOMATED PERFORMANCE METRICS DURING SUB-STITCHES OF VESICO-URETHRAL ANASTOMOSIS" @default.
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