{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T18:44:45Z","timestamp":1790966685815,"version":"4.1.0"},"reference-count":23,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T00:00:00Z","timestamp":1764460800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T00:00:00Z","timestamp":1764460800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J of Urology"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Operative time prediction is crucial for efficient operating room scheduling. This study developed and validated an operative time prediction system for robot\u2010assisted laparoscopic radical prostatectomy using data from two institutions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Retrospectively identified 557 and 150 patients who underwent RARP at Tohoku University Hospital and Miyagi Cancer Center were analyzed. The following variables were collected as explanatory variables for the prediction system: Age, height, body mass index, comorbidities, performance status, prostate volume, tumor extent of local invasion, grade group, prostatitis, preoperative treatment, history of intraperitoneal surgery, lymphadenectomy, and nerve\u2010sparing. In addition, the observed operative time was collected as a source variable for the objective variable. The observed operative time was calculated as the sum of components related to institutional, operator, and patient factors. Approximation curves were applied to the first two components, while a random forest\u2010based machine learning model was applied to the latter, resulting in the development of an integrated prediction system.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The normalized root mean square error between the observed and predicted operative times was 0.107 in internal validation and 0.148 in external validation, demonstrating greater reliability than the expected operative time by operators in advance. Factors that are known to impact operative time, such as lymphadenectomy, grade group, prostate volume, and body mass index, were identified as contributing variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The system provides a reliable and robust prediction focusing on factors known to impact operative time. It has the potential to improve the efficiency of operating room scheduling.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/iju.70292","type":"journal-article","created":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T09:21:13Z","timestamp":1764494473000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Operative Time Prediction by Machine Learning for Robot\u2010Assisted Laparoscopic Radical Prostatectomy"],"prefix":"10.1111","volume":"33","author":[{"given":"Yu","family":"Suzuki","sequence":"first","affiliation":[{"name":"Department of Urology Tohoku University Graduate School of Medicine  Sendai Miyagi Japan"},{"name":"AI Lab Tohoku University Hospital  Sendai Miyagi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3998-4612","authenticated-orcid":false,"given":"Shinya","family":"Sonobe","sequence":"additional","affiliation":[{"name":"AI Lab Tohoku University Hospital  Sendai Miyagi Japan"},{"name":"Department of Translational Neuroscience Tohoku University Graduate School of Medicine  Japan"},{"name":"Department of Neurosurgical Engineering, Graduate School of Biomedical Engineering Tohoku University  Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshihide","family":"Kawasaki","sequence":"additional","affiliation":[{"name":"Department of Urology Tohoku University Graduate School of Medicine  Sendai Miyagi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saori","family":"Ikumi","sequence":"additional","affiliation":[{"name":"AI Lab Tohoku University Hospital  Sendai Miyagi Japan"},{"name":"Department of Anesthesiology and Perioperative Medicine Tohoku University Graduate School of Medicine  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eichi","family":"Takaya","sequence":"additional","affiliation":[{"name":"AI Lab Tohoku University Hospital  Sendai Miyagi Japan"},{"name":"Department of Clinical Imaging Tohoku University Graduate School of Medicine  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos Makoto","family":"Miyauchi","sequence":"additional","affiliation":[{"name":"AI Lab Tohoku University Hospital  Sendai Miyagi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomoya","family":"Kobayashi","sequence":"additional","affiliation":[{"name":"AI Lab Tohoku University Hospital  Sendai Miyagi Japan"},{"name":"Department of Clinical Imaging Tohoku University Graduate School of Medicine  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junya","family":"Iwazaki","sequence":"additional","affiliation":[{"name":"AI Lab Tohoku University Hospital  Sendai Miyagi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hisanobu","family":"Adachi","sequence":"additional","affiliation":[{"name":"Division of Urology Miyagi Cancer Center  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S.Sano T.Yanase T.Ohta andM.Koyama Optuna: a next\u2010generation hyperparameter optimization framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2623\u20132631.","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_2_10_21_1","article-title":"A Unified Approach to Interpreting Model Predictions","author":"Lundberg S.","year":"2017","journal-title":"arXiv [Cs.AI]"},{"key":"e_1_2_10_22_1","first-page":"4765","article-title":"A Unified Approach to Interpreting Model Predictions","volume":"30","author":"Lundberg S. M.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_10_23_1","volume-title":"Principal Component Analysis","author":"Jolliffe I. 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