Fatih ŞendaǧBurak ZeybekAli Osman AkdemirBanu OzgurelKemal ÖztekinAkdemir, AliOzgurel, BanuOztekin, KemalZeybek, BurakSendag, Fatih2025-10-0620141478596X, 147859511478-59511478-596X10.1002/rcs.15672-s2.0-84908281400https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908281400&doi=10.1002%2Frcs.1567&partnerID=40&md5=a691a88e48a53e284ca00d571b52c6d6https://gcris.yasar.edu.tr/handle/123456789/9982https://doi.org/10.1002/rcs.1567Background: The objective was to evaluate the learning curve for performing a robotic hysterectomy to treat benign gynaecological disease. Methods: Thirty-six patients underwent robotic hysterectomy for benign indications. A systematic chart review of consecutive cases was conducted. The collected data included age BMI operating time set-up time docking time uterine weight blood loss intraoperative complications postoperative complications conversions to laparotomy and length of hospital stay. Results: The mean operating set-up and docking times were 169±54.5 52.9±12.4 and 7.8±7.6min respectively. The learning curve analysis revealed a decrease in both docking and operating times with both curves plateauing after case 9. Conclusions: The learning curve analysis revealed a decrease in docking time and operating time after case 9 suggesting that there might be a fast learning curve for experienced laparoscopic surgeons to master robotic hysterectomy and that the docking process does not have a significant negative influence on the overall operating time. © 2022 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessHysterectomy, Learning Curve, Robot, Da Vinci, Blood Loss, Gynaecological Disease, Hysterectomy, Intra-operative, Learning Curve Analysis, Learning Curves, Length Of Hospital Stays, Operating Time, Postoperative Complications, Set-up Time, Robotics, Adnexa Disease, Adult, Age, Article, Body Mass, Clinical Article, Conversion To Open Surgery, Female, Gynecologic Disease, Human, Hysterectomy, Laparoscopic Surgical Instrument, Laparotomy, Learning Curve, Length Of Stay, Medical Record Review, Middle Aged, Operation Duration, Operative Blood Loss, Peroperative Complication, Postoperative Complication, Robot Assisted Surgery, Robotic Hysterectomy, Salpingooophorectomy, Surgeon, Uterus Myoma, Uterus Weight, Education, Genital Diseases Female, Gynecologic Surgery, Gynecology, Multivariate Analysis, Pathology, Procedures, Robotic Surgical Procedure, Time, Uterus, Adult, Body Mass Index, Female, Gynecologic Surgical Procedures, Gynecology, Humans, Intraoperative Complications, Learning Curve, Length Of Stay, Middle Aged, Multivariate Analysis, Operative Time, Postoperative Complications, Robotic Surgical Procedures, Time Factors, UterusBlood loss, Gynaecological disease, Hysterectomy, Intra-operative, Learning curve analysis, Learning curves, Length of hospital stays, Operating time, Postoperative complications, Set-up time, Robotics, adnexa disease, adult, age, Article, body mass, clinical article, conversion to open surgery, female, gynecologic disease, human, hysterectomy, laparoscopic surgical instrument, laparotomy, learning curve, length of stay, medical record review, middle aged, operation duration, operative blood loss, peroperative complication, postoperative complication, robot assisted surgery, robotic hysterectomy, salpingooophorectomy, surgeon, uterus myoma, uterus weight, education, Genital Diseases Female, gynecologic surgery, gynecology, multivariate analysis, pathology, procedures, robotic surgical procedure, time, uterus, Adult, Body Mass Index, Female, Gynecologic Surgical Procedures, Gynecology, Humans, Intraoperative Complications, Learning Curve, Length of Stay, Middle Aged, Multivariate Analysis, Operative Time, Postoperative Complications, Robotic Surgical Procedures, Time Factors, UterusRobotHysterectomyLearning CurveArticle