Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes-Reference-Cited by-同舟云学术

Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes

Published:2018-05 Issue:5 Volume:32 Page:438-444
ISSN:0892-7790
Container-title:Journal of Endourology
language:en
Short-container-title:Journal of Endourology

Author:

Hung Andrew J.¹,Chen Jian¹,Che Zhengping²,Nilanon Tanachat²,Jarc Anthony³,Titus Micha¹,Oh Paul J.¹,Gill Inderbir S.¹,Liu Yan²

Affiliation:

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

2. USC Machine Learning Center, Viterbi School of Engineering, University of Southern California, Los Angeles, California.

3. Medical Research, Intuitive Surgical, Inc., Norcross, Georgia.

Publisher

Mary Ann Liebert Inc

Subject

Urology

Link

http://www.liebertpub.com/doi/pdf/10.1089/end.2018.0035

Reference16 articles.

1. Surgical Skill and Complication Rates after Bariatric Surgery

2. Teaching Surgical Skills — Changes in the Wind

3. Learning curves for urological procedures: a systematic review

4. Global Evaluative Assessment of Robotic Skills: Validation of a Clinical Assessment Tool to Measure Robotic Surgical Skills

5. Development and Validation of an Objective Scoring Tool for Robot-Assisted Radical Prostatectomy: Prostatectomy Assessment and Competency Evaluation

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