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Objectives: The research's purpose is to develop a software that automatically integrates and overlay 3D virtual models of kidneys harboring renal masses into the Da Vinci robotic console, assisting surgeon during the intervention., Introduction: Precision medicine, especially in the field of minimally-invasive partial nephrectomy, aims to use 3D virtual models as a guidance for augmented reality robotic procedures. However, the co-registration process of the virtual images over the real operative field is performed manually., Methods: In this prospective study, two strategies for the automatic overlapping of the model over the real kidney were explored: the computer vision technology, leveraging the super-enhancement of the kidney allowed by the intraoperative injection of Indocyanine green for superimposition and the convolutional neural network technology, based on the processing of live images from the endoscope, after a training of the software on frames from prerecorded videos of the same surgery. The work-team, comprising a bioengineer, a software-developer and a surgeon, collaborated to create hyper-accuracy 3D models for automatic 3D-AR-guided RAPN. For each patient, demographic and clinical data were collected., Results: Two groups (group A for the first technology with 12 patients and group B for the second technology with 8 patients) were defined. They showed comparable preoperative and post-operative characteristics. Concerning the first technology the average co-registration time was 7 (3-11) seconds while in the case of the second technology 11 (6-13) seconds. No major intraoperative or postoperative complications were recorded. There were no differences in terms of functional outcomes between the groups at every time-point considered., Conclusion: The first technology allowed a successful anchoring of the 3D model to the kidney, despite minimal manual refinements. The second technology improved kidney automatic detection without relying on indocyanine injection, resulting in better organ boundaries identification during tests. Further studies are needed to confirm this preliminary evidence., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: An empirical selective clamping strategy based on the direction of the arterial branches can lead to failures during partial nephrectomy, even when assisted by three-dimensional virtual models (3DVMs)., Objective: To develop and test new 3DVMs that include kidney perfusion regions and evaluate their intraoperative accuracy in guiding selective clamping and their impact on postoperative renal function., Design, Setting, and Participants: For patients with a kidney suitable for nephron-sparing surgery, 3DVMs were supplemented with a Voronoi diagram, a Euclidean distance-based mathematical tool, to calculate vascular-dominant regions the kidney., Surgical Procedure: Robot-assisted partial nephrectomy guided by perfusion-region (PR)-3DVMs., Measurements: All anatomic information given by the PR-3DVMs was collected. Selective or superselective clamping was planned and performed intraoperatively when feasible under 3DVM assistance. Changes in split renal function (SRF) and estimated renal plasmatic flow (ERPF) were evaluated for 51 patients who underwent baseline and 3-mo postoperative renal scintigraphy., Results and Limitations: A total of 103 patients were prospectively enrolled. The median number of kidney and tumor perfusion regions were 8 (interquartile range [IQR] 7-10) and 3 (IQR 2-3), respectively. A clampless, selective clamping, and global clamping strategy was applied in eight (7.8%), 79 (76.6%), and 16 (15.5%) cases, respectively, with no differences between planning and surgery in terms of the number or order of arteries clamped or the perfusion regions that underwent ischemia. Among the 51 patients who underwent renal scintigraphy, the mean SRF decreased by 11.3%, 7.7%, and 1.7% after global, selective, and superselective clamping, respectively (p = 0.004). Similar results were obtained for ERPF (18.9%, 9.9%, and 6.0%; p = 0.02). The main limitation is the need for a bioengineer to manually refine the 3DVMs., Conclusions: Use of mathematical algorithms for 3DVMs allows precise estimation of kidney perfusion regions to maximize the efficacy of selective clamping and minimize renal function impairment., Patient Summary: Three-dimensional models that include regions of blood flow to the kidney can be used to guide clamping of blood vessels when part of the kidney is being surgically removed. More limited clamping can reduce damage to the remaining portion of the kidney and result in better recovery of kidney function after surgery., (Copyright © 2023 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Selective clamping during robot-assisted partial nephrectomy (RAPN) may reduce ischemia-related functional impairment. The intraoperative use of 3D-virtual models (3DVMs) can improve surgical planning, resulting in a greater success rate for selective clamping. Our goal is to introduce a new generation of 3DVMs, which consider the perfusion volumes of the kidney. Patients listed for RAPN from 2021 to 2022 were recruited. A selective clamping strategy was designed and intraoperatively performed based on the specifically generated 3DVMs. The effectiveness of selective clamping was evaluated using near-infrared-fluorescence imaging (NIRF) and 3DVM. Perfusion areas extensions were compared, and relevant preoperative characteristics were analyzed. In 61 of 80 (76.25%) cases, selective clamping was performed. The concordance between the 3DVM areas and the NIRF-enhanced areas was verified (k = 0.91). According to the distribution of perfused areas crossing the tumor, there were one, two, three, four, and five crossing areas, with relative perfusion rates of 13.75%, 35%, 32.5%, 13.75%, and 5%, respectively. Lesion diameter and mesorenal location were the only factors related to a higher number (>3) of perfusion volumes crossing the lesion. The implementation of mathematical algorithms to 3DVMs allows for precise estimation of the perfusion zone of each arterial branch feeding the organ, leading to the performance of safe and effective pedicle management planning.