Use of Automated Performance Metrics to Measure Surgeon Performance during Robotic Vesicourethral Anastomosis and Methodical Development of a Training Tutorial

Chen, Jian; Oh, Paul; Cheng, Nathan; Shah, Ankeet; Montez, Jeremy; Jarc, Anthony; Guo, Liheng; Gill, Inderbir S.; Hung, Andrew J.

doi:10.1016/j.juro.2018.05.080

Cited by 59 publications

(48 citation statements)

References 22 publications

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“…These instrument motion‐tracking and events metrics are derived from computer‐based data recording devices. Preliminary studies have shown that APMs can differentiate expert and novice surgeon performance in both laboratory and clinical settings . Our group has shown that APMs differentiate surgeon experience during the steps of RARP .…”

Section: Introductionmentioning

confidence: 81%

“…Baseline differences between patients in Group 1/ Experience and Group 2/Experience included preoperative IPSS (5 vs 4; P = 0.004) and prostate volume (50 vs 44 mL; P = 0.011 [ Table 7]). Group 1/Experience included a higher proportion of nerve-sparing procedures (83.4 vs 66.7%; P = [1][2][3][4][5][6][7][8][9] days; P = 0.049), and lower rate of low-grade (Clavien grade I-II) postoperative complications (6.7 vs 15.9%; P = 0.038). There was no significant difference in continence, erectile function or biochemical recurrence at 3, 6, 12, 18 or 24 months postoperatively (P > 0.05).…”

Section: Historical Cases Comparison Between Group 1/ Experience and mentioning

confidence: 99%

“…Preliminary studies have shown that APMs can differentiate expert and novice surgeon performance in both laboratory and clinical settings . Our group has shown that APMs differentiate surgeon experience during the steps of RARP . Furthermore, we have shown that APMs, with the aid of machine‐learning (ML) algorithms, can predict peri‐operative short‐term clinical outcomes after RARP .…”

Section: Introductionmentioning

confidence: 97%

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A deep‐learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot‐assisted radical prostatectomy

et al. 2019

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ResultsContinence was attained in 79 patients (79%) after a median of 126 days. The DL model achieved a C-index of 0.6 and an MAE of 85.9 in predicting continence. APMs were ranked higher by the model than clinicopathological features. In the historical cohort, patients in Group 1/APMs had superior rates of urinary continence at 3 and 6 months postoperatively (47.5 vs 36.7%, P = 0.034, and 68.3 vs 59.2%, P = 0.047, respectively). ConclusionUsing APMs and clinicopathological data, the DeepSurv DL model was able to predict continence after RARP. In this feasibility study, surgeons with more efficient APMs achieved higher continence rates at 3 and 6 months after RARP.Keywords robotic surgical procedures, prostatectomy, artificial intelligence, urinary incontinence, quality of life

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Section: Introductionmentioning

confidence: 81%

Section: Historical Cases Comparison Between Group 1/ Experience and mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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A deep‐learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot‐assisted radical prostatectomy

et al. 2019

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“…We also found that less robotic instrument articulation was used whilst operating in a deeper pelvis. In our prior study, more instrument articulation was an indicator of greater robotic surgical expertise . Specifically, expert surgeons (with >100 prior robotic surgical cases) had more instrument joint articulation during the VUA step than surgeons with less experience.…”

Section: Discussionmentioning

confidence: 88%

“…We assessed the influence of body habitus, bony pelvic measurements, prostate measurements, and surgeon experience (robotic surgical expert [with >100 prior robotic surgical cases] vs novice [with <100 prior robotic surgical cases]) on surgical performance, i.e., APMs, using multivariable linear regression analysis. Our prior work has confirmed that differences in surgical skills are apparent after 100 cases . As dropping bladder, vesicourethral anastomosis (VUA) and bilateral pelvic lymph node dissection (PLND) do not involve the prostate, no prostate measurements were included in analysis during these steps.…”

Section: Patients Subjects and Methodsmentioning

confidence: 99%

Effect of surgeon experience and bony pelvic dimensions on surgical performance and patient outcomes in robot‐assisted radical prostatectomy

et al. 2019

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Objectives To evaluate the effects of surgeon experience, body habitus, and bony pelvic dimensions on surgeon performance and patient outcomes after robot‐assisted radical prostatectomy (RARP). Patients, Subjects and Methods The pelvic dimensions of 78 RARP patients were measured on preoperative magnetic resonance imaging and computed tomography by three radiologists. Surgeon automated performance metrics (APMs [instrument motion tracking and system events data, i.e., camera movement, third‐arm swap, energy use]) were obtained by a systems data recorder (Intuitive Surgical, Sunnyvale, CA, USA) during RARP. Two analyses were performed: Analysis 1, examined effects of patient characteristics, pelvic dimensions and prior surgeon RARP caseload on APMs using linear regression; Analysis 2, the effects of patient body habitus, bony pelvic measurement, and surgeon experience on short‐ and long‐term outcomes were analysed by multivariable regression. Results Analysis 1 showed that while surgeon experience affected the greatest number of APMs (P < 0.044), the patient's body mass index, bony pelvic dimensions, and prostate size also affected APMs during each surgical step (P < 0.043, P < 0.046, P < 0.034, respectively). Analysis 2 showed that RARP duration was significantly affected by pelvic depth (β = 13.7, P = 0.039) and prostate volume (β = 0.5, P = 0.024). A wider and shallower pelvis was less likely to result in a positive margin (odds ratio 0.25, 95% confidence interval [CI] 0.09–0.72). On multivariate analysis, urinary continence recovery was associated with surgeon's prior RARP experience (hazard ratio [HR] 2.38, 95% CI 1.18–4.81; P = 0.015), but not on pelvic dimensions (HR 1.44, 95% CI 0.95–2.17). Conclusion Limited surgical workspace, due to a narrower and deeper pelvis, does affect surgeon performance and patient outcomes, most notably in longer surgery time and an increased positive margin rate.

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Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications

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Key Points Question What is the utility of a data set that contains videos of surgeons managing hemorrhage? Findings This quality improvement study of the Simulated Outcomes Following Carotid Artery Laceration (SOCAL), a public data set of surgeons managing catastrophic surgical hemorrhage in a cadaveric training exercise included 65 071 instrument annotations with recorded outcomes. Computer vision–based instrument detection achieved a mean average precision of 0.67 on SOCAL and a sensitivity of 0.77 and a positive predictive value of 0.96 at detecting surgical instruments from real intraoperative video. Meaning A corpus of videos of surgeons managing catastrophic hemorrhage is a novel, valuable resource for surgical data science.

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Use of Automated Performance Metrics to Measure Surgeon Performance during Robotic Vesicourethral Anastomosis and Methodical Development of a Training Tutorial

Cited by 59 publications

References 22 publications

A deep‐learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot‐assisted radical prostatectomy

A deep‐learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot‐assisted radical prostatectomy

Effect of surgeon experience and bony pelvic dimensions on surgical performance and patient outcomes in robot‐assisted radical prostatectomy

Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications

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