{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T18:23:23Z","timestamp":1790101403370,"version":"4.0.1"},"reference-count":35,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Reports"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>We recently reported the importance of deep learning (DL) of pelvic magnetic resonance imaging in predicting the degree of urinary incontinence (UI) following robot\u2010assisted radical prostatectomy (RARP). However, our results were limited because the prediction accuracy was approximately 70%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Aim<\/jats:title><jats:p>To develop a more precise prediction model that can inform patients about UI recovery post\u2010RARP surgery using a DL model based on intraoperative video images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and Results<\/jats:title><jats:p>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 evaluated, including pre\u2010 and intraoperative parameters. We evaluated the DL model plus simple or ensemble machine learning (ML), and the area under the receiver operating characteristic curve (AUC) was analyzed through sensitivity and specificity. Of 101, 64 and 37 patients demonstrated \u201cearly continence (using 0 or 1 safety pad at 3 months post\u2010RARP)\u201d and \u201clate continence (others),\u201d respectively, at 3\u2009months postoperatively. The combination of DL and simple ML using intraoperative video snapshots with clinicopathological parameters had a notably high performance (AUC, 0.683\u20130.749) to predict early recovery from UI after surgery. Furthermore, combining DL with ensemble artificial neural network using intraoperative video snapshots had the highest performance (AUC, 0.882; sensitivity, 92.2%; specificity, 78.4%; overall accuracy, 85.3%) to predict early recovery from post\u2010RARP incontinence, with similar results by internal validation. The addition of clinicopathological parameters showed no additive effects for each analysis using DL, EL and simple ML.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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\u2010term UI and pad\u2010free continence.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cnr2.1861","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T08:10:25Z","timestamp":1689322225000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Combination of deep learning and ensemble machine learning using intraoperative video images strongly predicts recovery of urinary continence after robot\u2010assisted radical prostatectomy"],"prefix":"10.1002","volume":"6","author":[{"given":"Wataru","family":"Nakamura","sequence":"first","affiliation":[{"name":"Department of Urology, School of Medicine Fujita Health University  Toyoake Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2589-922X","authenticated-orcid":false,"given":"Makoto","family":"Sumitomo","sequence":"additional","affiliation":[{"name":"Department of Urology, School of Medicine Fujita Health University  Toyoake Japan"},{"name":"Fujita Cancer Center Fujita Health University  Toyoake Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenji","family":"Zennami","sequence":"additional","affiliation":[{"name":"Department of Urology, School of Medicine Fujita Health University  Toyoake Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masashi","family":"Takenaka","sequence":"additional","affiliation":[{"name":"Department of Urology, School of Medicine 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