Learning Curve Analysis of Single-Incision Ovarian Cystectomy: Comparative Study of Robotic and Conventional Laparoscopic Techniques

Author:

Kim Seongmin1ORCID,Lee Seon-Mi2ORCID,Seol Aeran2,Lee Sanghoon2ORCID,Song Jae-Yun2,Lee Jae-Kwan2ORCID,Lee Nak-Woo2

Affiliation:

1. Gynecologic Cancer Center, CHA Ilsan Medical Center, CHA University College of Medicine, 1205 Jungang-ro, Ilsandong-gu, Goyang-si 10414, Republic of Korea

2. Department of Obstetrics and Gynecology, Korea University College of Medicine, 73 Inchon-ro, Seongbuk-gu, Seoul 02841, Republic of Korea

Abstract

Ovarian cystectomy, aimed at preserving fertility, has advanced through minimally invasive surgical techniques. This study evaluates the learning curves and surgical outcomes of three such approaches: DaVinci Robotic Single-Site (RSS), DaVinci Robotic Single-Port (RSP), and laparo-endoscopic single-site surgery (LESS). To analyze the learning curves and surgical outcomes for these techniques, providing insights into their effectiveness and proficiency development. Retrospective analysis of 104 patients with ovarian tumors, divided into RSS (n = 52), RSP (n = 22), and LESS (n = 30) groups. Metrics analyzed included age, BMI, tumor size, hemoglobin drop, operative time, docking time, console time, and tumor location. No significant differences in age, BMI, transfusion rate, hemoglobin drop, or length of stay were found among the groups. RSS had larger tumors on average, and LESS had a higher occurrence rate on the right side. LESS demonstrated the shortest operative time, while RSS and RSP had comparable times. Docking and console times did not differ significantly between RSS and RSP. RSP reached proficiency faster than RSS in docking and console times, while LESS exhibited the greatest variability in operative time. RSP offers a faster and more consistent learning curve, making it advantageous for complex procedures, whereas LESS provides shorter operative times but with higher variability. These findings are crucial for surgical training and resource allocation in medical institutions.

Publisher

MDPI AG

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3