Combination of deep learning and ensemble machine learning using intraoperative video images strongly predicts recovery of urinary continence after robot-assisted radical prostatectomy.
Wataru NakamuraMakoto SumitomoKenji ZennamiMasashi TakenakaManabu IchinoKiyoshi TakaharaAtsushi TeramotoRyoichi ShirokiPublished in: Cancer reports (Hoboken, N.J.) (2023)
Our findings suggest that the DL algorithm with intraoperative video imaging is a reliable method for informing patients about the severity of their recovery from UI after RARP, although it is not clear if our methods are reproducible for predicting long-term UI and pad-free continence.
Keyphrases
- robot assisted
- deep learning
- machine learning
- radical prostatectomy
- prostate cancer
- convolutional neural network
- minimally invasive
- artificial intelligence
- end stage renal disease
- newly diagnosed
- ejection fraction
- high resolution
- patients undergoing
- prognostic factors
- big data
- optical coherence tomography
- patient reported