Deep Learning to Automate Technical Skills Assessment in Robotic Surgery-Reference-Cited by-同舟云学术

Deep Learning to Automate Technical Skills Assessment in Robotic Surgery

Published:2021-11-01 Issue:11 Volume:156 Page:1059
ISSN:2168-6254
Container-title:JAMA Surgery
language:en
Short-container-title:JAMA Surg

Author:

Hung Andrew J.¹,Liu Yan²,Anandkumar Animashree³

Affiliation:

1. Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology USC Institute of Urology, University of Southern California, Los Angeles

2. Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles

3. Department of Computing & Mathematical Sciences, Caltech, Pasadena, California

Publisher

American Medical Association (AMA)

Subject

Surgery

Link

https://jamanetwork.com/journals/jamasurgery/articlepdf/2784278/jamasurgery_hung_2021_si_210004_1636049311.62775.pdf

Reference7 articles.

1. Surgical skill and complication rates after bariatric surgery.;Birkmeyer;N Engl J Med,2013

2. Measuring to improve: peer and crowd-sourced assessments of technical skill with robot-assisted radical prostatectomy.;Ghani;Eur Urol,2016

3. Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery.;Hung;JAMA Surg,2018

4. A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy.;Hung;BJU Int,2019

5. Survival analysis using surgeon skill metrics and patient factors to predict urinary continence recovery after robot-assisted radical prostatectomy.;Trinh;Eur Urol Focus

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