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BJU Int. 2019 Feb 27. doi: 10.1111/bju.14735. [Epub ahead of print]

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

Author information

1
Center for Robotic Simulation and Education, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
2
School of Electronics Engineering and Computer Science, Peking University, Beijing, China.
3
Department of Information Systems, University of Maryland, Baltimore, MD, USA.
4
Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.

Abstract

OBJECTIVES:

To predict urinary continence recovery after robot-assisted radical prostatectomy (RARP) using a deep learning (DL) model, which was then used to evaluate surgeon's historical patient outcomes.

SUBJECTS AND METHODS:

Robotic surgical automated performance metrics (APMs) during RARP, and patient clinicopathological and continence data were captured prospectively from 100 contemporary RARPs. We used a DL model (DeepSurv) to predict postoperative urinary continence. Model features were ranked based on their importance in prediction. We stratified eight surgeons based on the five top-ranked features. The top four surgeons were categorized in 'Group 1/APMs', while the remaining four were categorized in 'Group 2/APMs'. A separate historical cohort of RARPs (January 2015 to August 2016) performed by these two surgeon groups was then used for comparison. Concordance index (C-index) and mean absolute error (MAE) were used to measure the model's prediction performance. Outcomes of historical cases were compared using the Kruskal-Wallis, chi-squared and Fisher's exact tests.

RESULTS:

Continence 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).

CONCLUSION:

Using 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:

artificial intelligence; prostatectomy; quality of life; robotic surgical procedures; urinary incontinence

PMID:
30811828
DOI:
10.1111/bju.14735

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