Traverso Paolo, Mantica Guglielmo, Giasotto Veronica, Terrone Carlo
Department of Surgical and Diagnostic Integrated Sciences (DISC), University of Genova, Genova, Italy.
IRCCS Policlinico San Martino, Genova, Italy.
Res Rep Urol. 2025 Feb 6;17:27-30. doi: 10.2147/RRU.S503524. eCollection 2025.
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.
三维模型已被引入作为提高机器人辅助部分肾切除术（RAPN）期间外科医生精准度的工具。它们显示出能提供准确的解剖细节，通过减少并发症来缩短手术时间并提高患者安全性。在过去几年里，已经开发并提出了几种有用的模型。然而，关于操作者的经验以及学习曲线是否以及如何影响模型的准确性和精准度，文献仍然很少。有鉴于此，本研究的目的是从CT图像开始评估RAPN三维术前模型分割的准确性、精准度的人际变异性以及学习曲线。本研究将确定操作者经验和学习曲线对RAPN三维术前模型准确性的影响，优化工作流程以实现更广泛的临床应用。
