Combination of deep learning and ensemble machine learning using intraoperative video images strongly predicts recovery of urinary continence after robot-assisted radical prostatectomy.

ABSTRACT:

Background

We recently reported the importance of deep learning (DL) of pelvic magnetic resonance imaging in predicting the degree of urinary incontinence (UI) following robot-assisted radical prostatectomy (RARP). However, our results were limited because the prediction accuracy was approximately 70%.

Aim

To develop a more precise prediction model that can inform patients about UI recovery post-RARP surgery using a DL model based on intraoperative video images.

Methods and results

The study cohort comprised of 101 patients with localized prostate cancer undergoing RARP. Three snapshots from intraoperative video recordings showing the pelvic cavity (prior to bladder neck incision, immediately following prostate removal, and after vesicourethral anastomosis) were evalu

SUBMITTER: Nakamura W 

PROVIDER: S-EPMC10480482 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature