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Review
. 2018 Aug 1;153(8):770-771.
doi: 10.1001/jamasurg.2018.1512.

Automated Performance Metrics and Machine Learning Algorithms to Measure Surgeon Performance and Anticipate Clinical Outcomes in Robotic Surgery

Andrew J Hung  1 , Jian Chen  1 , Inderbir S Gill  1
Affiliations
Review

Automated Performance Metrics and Machine Learning Algorithms to Measure Surgeon Performance and Anticipate Clinical Outcomes in Robotic Surgery

Andrew J Hung et al. JAMA Surg. .
No abstract available

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Figures

Figure.
Figure.
Training a Machine Learning (ML) Algorithm to Estimate Perioperative Clinical Outcome With Automated Performance Metrics (APMs) (1) A set of 25 APMs is captured from each of 8 distinct surgical steps during robot-assisted radical prostatectomy. (2) Cases are labeled as LOS ≤2 d or LOS >2 d based on hospital length of stay (LOS) after surgery. (3) A random forest 50-tree ML algorithm is trained with APMs and the clinical outcome label. (4) The trained ML algorithm can then estimate clinical outcomes (LOS ≤2 d or LOS >2 d) of a new case by using APMs only.
See this image and copyright information in PMC

References

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