Three-Dimensional Virtual Model for Robot-Assisted Partial Nephrectomy (RAPN): Development of Study Protocol for Evaluation of the Learning Curve to Optimize the Precision and Accuracy of the 3D Imaging.
Paolo Traverso, Guglielmo Mantica, Veronica Giasotto, Carlo Terrone
{"title":"Three-Dimensional Virtual Model for Robot-Assisted Partial Nephrectomy (RAPN): Development of Study Protocol for Evaluation of the Learning Curve to Optimize the Precision and Accuracy of the 3D Imaging.","authors":"Paolo Traverso, Guglielmo Mantica, Veronica Giasotto, Carlo Terrone","doi":"10.2147/RRU.S503524","DOIUrl":null,"url":null,"abstract":"<p><p>3D models have been introduced as tools to improve surgeon's precision during Robotic-Assisted Partial Nephrectomy (RAPN). They showed to provide accurate anatomical details, improve operative time and patient safety by reducing complications. Over the last years, several useful models have been developed and proposed. However, literature is still scant regarding if and how the experience of the operator, and the learning curve, may impact the accuracy and precision of the model. In this light, the aim of the study is to evaluate the accuracy, the interpersonal variability of the precision and the learning curve for the segmentation of RAPN 3D preoperative models starting from CT images. This study will identify the influence of operator experience and learning curves on the accuracy of 3D preoperative models in RAPN, optimizing workflows for broader clinical adoption.</p>","PeriodicalId":21008,"journal":{"name":"Research and Reports in Urology","volume":"17 ","pages":"27-30"},"PeriodicalIF":2.7000,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11809357/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research and Reports in Urology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2147/RRU.S503524","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
3D models have been introduced as tools to improve surgeon's precision during Robotic-Assisted Partial Nephrectomy (RAPN). They showed to provide accurate anatomical details, improve operative time and patient safety by reducing complications. Over the last years, several useful models have been developed and proposed. However, literature is still scant regarding if and how the experience of the operator, and the learning curve, may impact the accuracy and precision of the model. In this light, the aim of the study is to evaluate the accuracy, the interpersonal variability of the precision and the learning curve for the segmentation of RAPN 3D preoperative models starting from CT images. This study will identify the influence of operator experience and learning curves on the accuracy of 3D preoperative models in RAPN, optimizing workflows for broader clinical adoption.
期刊介绍：
Research and Reports in Urology is an international, peer-reviewed, open access, online journal. Publishing original research, reports, editorials, reviews and commentaries on all aspects of adult and pediatric urology in the clinic and laboratory including the following topics: Pathology, pathophysiology of urological disease Investigation and treatment of urological disease Pharmacology of drugs used for the treatment of urological disease Although the main focus of the journal is to publish research and clinical results in humans; preclinical, animal and in vitro studies will be published where they will shed light on disease processes and potential new therapies. Issues of patient safety and quality of care will also be considered.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
