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Robotic surgery has recently been used for treatment of renal cell carcinoma (RCC) and neoplastic thrombus located in the renal vein or inferior vena cava (IVC). Accurate identification of the thrombus location is crucial, and three-dimensional augmented reality (3D AR) may be valuable in achieving this. We enrolled patients with nonmetastatic RCC and level 0–I venous thrombus (Mayo Clinic classification) for robot-assisted radical nephrectomy and thrombectomy with 3D AR guidance. Five patients were prospectively enrolled; three had a level 0 thrombus and two had a level I thrombus. The mean operative time was 123 ± 15 min, mean IVC clamping time was 9.4 ± 6.8 min, and mean estimated blood loss was 750 ± 150 ml. The AR system allowed precise estimation of the thrombus location in all cases. No intraoperative complications or postoperative Clavien-Dindo grade >2 complications occurred. Use of 3D AR guidance allowed correct estimation of the limits of the thrombus and guided the surgeon in selecting an appropriate surgical strategy.

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.

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.

Objective: Indocyanine green (ICG) and Three-dimensional virtual models (3DVMs) have been developed and introduced in surgical daily practice in the last years to improve preoperative planning and efficacy of selective clamping during nephron sparing surgery. Aim of the study is to evaluate ICG and 3DVMs usefulness in guiding the surgeon's strategy during robot-assisted partial nephrectomy (RAPN). Materials and methods: We retrospectively reviewed our institutional database and analysed all patients harboring organ-confined renal masses underwent RAPN from 2018 to 2023. Specifically, we included all the patients with a 3DVMs available for preoperative planning and intraoperative surgical navigation. In those cases in which a selective clamping was planned and intraoperatively preformed, ICG solution was injected allowing visualization of the kidney's perfusion regions during the surgery. All demographic, intraoperative, postoperative and functional data were extracted and evaluated, comparing the 3DVM-ICG selective clamping with the global clamping Group. Results: Overall, 212 patients were included. 111 (51%) patients underwent 3DVM-ICG selective clamping RAPNs. No differences were recorded in terms of preoperative or perioperative variables between the global and the selective clamping group, except for a lower operative time for the global clamping group (93.5 min vs. 100 min, p = 0.02). Focusing on the functional outcomes, the selective clamping group showed better postoperative outcomes in terms of ΔeGFR at every timepoint considered (postoperative: -10.4% vs. -16.7%, p = 0.003; 3rd month: -9% vs. -14%, p = 0.01; 6th month: -8.9% vs. -13.8%, p = 0.04). Conclusions: ICG and 3DVMs can be considered the most useful and advanced tools to plan an effective selective clamping strategy during RAPN. They can be used in different ways, matching the surgeon's needs from the planning phase to the time of the surgery, leading towards maximum safety and efficacy outcomes.

The aim of “Precision Surgery” is to reduce the impact of surgeries on patients’ global health. In this context, over the last years, the use of three-dimensional virtual models (3DVMs) of organs has allowed for intraoperative guidance, showing hidden anatomical targets, thus limiting healthy-tissue dissections and subsequent damage during an operation. In order to provide an automatic 3DVM overlapping in the surgical field, we developed and tested a new software, called “ikidney”, based on convolutional neural networks (CNNs). From January 2022 to April 2023, patients affected by organ-confined renal masses amenable to RAPN were enrolled. A bioengineer, a software developer, and a surgeon collaborated to create hyper-accurate 3D models for automatic 3D AR-guided RAPN, using CNNs. For each patient, demographic and clinical data were collected. A total of 13 patients were included in the present study. The average anchoring time was 11 (6–13) s. Unintended 3D-model automatic co-registration temporary failures happened in a static setting in one patient, while this happened in one patient in a dynamic setting. There was one failure; in this single case, an ultrasound drop-in probe was used to detect the neoplasm, and the surgery was performed under ultrasound guidance instead of AR guidance. No major intraoperative nor postoperative complications (i.e., Clavien Dindo > 2) were recorded. The employment of AI has unveiled several new scenarios in clinical practice, thanks to its ability to perform specific tasks autonomously. We employed CNNs for an automatic 3DVM overlapping during RAPN, thus improving the accuracy of the superimposition process.

Objective: In the last years selective clamping strategies have been tested during partial nephrectomy (PN) to decrease the functional impairment linked to the ischemic damage. Recently, 3D virtual models (3DVM) have been reported to increase the accuracy of surgical strategy, leading to a higher rate of successful selective clamping. However, sometimes this empirical definition of vascular regions, based on arterial branches’ direction, fails intraoperatively and forcing towards a global clamping.Our aim is to present our new generation of 3DVM, built taking in consideration the different perfusion areas of the kidney instead of vascular regions empirically estimated, and to evaluate their accuracy intraoperatively and to assess if there are factors influencing the tumor location relative to these perfusion areas. Patients and surgical procedure: By using a mathematical tool, the Voronoi diagram, we calculate vascular perfusion regions of the organs and built the 3DVMs for all consecutive patients scheduled in our center for robot-assisted PN (RAPN) from 2019 to 2021. Based on the info given by the 3DVMs, a selective clamping strategy was planned end performed intraoperatively if feasible. To assess selective clamping success, near-infrared-fluorescence imaging (NIRF) was used. The concordance between virtual and real perfusion regions was automatically assessed by superimposition of 3DVMs over the real endoscopic view. The comparison between their extension was then assessed via k-Cohen test.At last, each lesion location was evaluated in relation to the number of its crossing perfused areas and a multivariate regression model (MLR) was fitted to assess potentially related preoperative factors. Results: 48 patients were enrolled. The renal pedicle management strategy was conducted as preoperatively planned in 100% of them. 68.7% of cases underwent selective clamping. The concordance between the NIRF enhanced areas and their corresponding 3DVM areas was confirmed (k = 0.94). Distribution of perfused areas crossing the tumor was as follows: 16.6%, 29.1%, 43.7%, 8.3%, 2.4% for 1, 2, 3, 4 and 5 crossing areas respectively. At MLR the clear cell histotype was the only factor related to a lower number (3) of perfusion areas. Conclusions: The implementation of mathematical algorithms to 3DVMs allows a precise estimation of the perfusion area of each arterial branch feeding the organ, leading to perform an effective pedicle management. Moreover, with this new technology it is possible to open new perspectives on the knowledge of renal tumors features, considering their growth in relation to their surrounding environment.

More than ever, precision surgery is making its way into modern surgery for functional organ preservation. This is possible mainly due to the increasing number of technologies available, including 3D models, virtual reality, augmented reality, and artificial intelligence. Intraoperative surgical navigation represents an interesting application of these technologies, allowing to understand in detail the surgical anatomy, planning a patient-tailored approach. Automatic superimposition comes into this context to optimally perform surgery as accurately as possible. Through a dedicated software (the first version) called iKidney, it is possible to superimpose the images using 3D models and live endoscopic images during partial nephrectomy, targeting the renal mass only. The patient is 31 years old with a 28 mm totally endophytic right-sided renal mass, with a PADUA score of 9. Thanks to the automatic superimposition and selective clamping, an enucleoresection of the renal mass alone was performed with no major postoperative complication (i.e., Clavien–Dindo < 2). iKidney-guided partial nephrectomy is safe, feasible, and yields excellent results in terms of organ preservation and functional outcomes. Further validation studies are needed to improve the prototype software, particularly to improve the rotational axes and avoid human help. Furthermore, it is important to reduce the costs associated with these technologies to increase its use in smaller hospitals.

Background: Some renal tumors have an imperative indication for nephron-sparing surgery (NSS), such as in cases of chronic kidney disease and bilateral complex tumors. Objective: To demonstrate the degree to which three-dimensional virtual model (3DVM) assistance can be helpful in planning the surgical strategy for high-complexity renal masses with an imperative indication for NSS. Design, setting, and participants: Three patients with high-complexity renal masses with unusual anatomy and an imperative indication for NSS were prospectively selected across 2020 and 2021 at our institution. All patients underwent contrast-enhanced computed tomography from which a 3DVM was obtained. Surgical procedure: Robot-assisted partial nephrectomy with 3DVM augmented reality guidance. Measurements: Demographics and tumor-related features were recorded. Data for intraoperative, pathological, and functional assessments were collected for all three patients. Results and limitations: Two of the three patients harbored bilateral renal tumors. The third patient presented with a renal mass in the left kidney and contralateral renal hypoplasia (right-split renal function of 25%). All of the patients demonstrated similar anatomical and tumor features on 3DVMs, with potentially independent vascularization and drainage for the lower pole. In one patient the upper pole of the kidney was spared, exiting in a functionally excluded hydrocalyx, while in the other two cases the upper pole was removed together with the lesion. The spared portion of the kidney retained vascularization, as demonstrated by intraoperative ultrasound and indocyanine green injection. The small sample size and short follow-up are the main limitations of the study. Conclusions: 3DVMs, especially for complex renal masses with an imperative indication for NSS, allow planning of the surgical strategy on the basis of the anatomical characteristics of the organ in which the tumor is growing. Patient summary: Three-dimensional models help in defining the best surgical strategy for kidney tumors, especially for complex tumors that require surgery to spare as much of the kidney as possible.

Recently, 3D models (3DM) gained popularity in urology, especially in nephron-sparing interventions (NSI). Up to now, the application of artificial intelligence (AI) techniques alone does not allow us to obtain a 3DM adequate to plan a robot-assisted partial nephrectomy (RAPN). Integration of AI with computer vision algorithms seems promising as it allows to speed up the process. Herein, we present a 3DM realized with the integration of AI and a computer vision approach (CVA), displaying the utility of AI-based Hyper Accuracy Three-dimensional (HA3D®) models in preoperative planning and intraoperative decision-making process of challenging robotic NSI. A 54-year-old Caucasian female with no past medical history was referred to the urologist for incidental detection of the right renal mass. Preoperative contrast-enhanced abdominal CT confirmed a 35 × 25 mm lesion on the anterior surface of the upper pole (PADUA 7), with no signs of distant metastasis. CT images in DICOM format were processed to obtain a HA3D® model. RAPN was performed using Da Vinci Xi surgical system in a three-arm configuration. The enucleation strategy was achieved after selective clamping of the tumor-feeding artery. Overall operative time was 85 min (14 min of warm ischemia time). No intra-, peri- and post-operative complications were recorded. Histopathological examination revealed a ccRCC (stage pT1aNxMx). AI is breaking new ground in medical image analysis panorama, with enormous potential in organ/tissue classification and segmentation, thus obtaining 3DM automatically and repetitively. Realized with the integration of AI and CVA, the results of our 3DM were accurate as demonstrated during NSI, proving the potentialities of this approach for HA3D® models’ reconstruction.