Robotic Vena Cava Thrombectomy with Three-dimensional Augmented Reality Guidance.

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.

Automatic 3D Augmented-Reality Robot-Assisted Partial Nephrectomy Using Machine Learning: Our Pioneer Experience.

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.

Computer Vision and Machine-Learning Techniques for Automatic 3D Virtual Images Overlapping During Augmented Reality Guided Robotic Partial Nephrectomy.

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.

Metaverse Surgical Planning with Three-dimensional Virtual Models for Minimally Invasive Partial Nephrectomy.

The recent integration of new virtual visualization modalities with artificial intelligence and high-speed internet connection has opened the door to the advent of the metaverse in medicine. In this totally virtual environment, three-dimensional virtual models (3DVMs) of the patient's anatomy can be visualized and discussed via digital avatars. Here we present for the first time a metaverse preoperative clinical case discussion before minimally invasive partial nephrectomy. The surgeons' digital avatars met in a virtual room and participated in a virtual consultation on the surgical strategy and clamping approach before the procedure. Robotic or laparoscopic procedures are then carried out according to the simulated surgical strategy. We demonstrate how this immersive virtual reality experience overcomes the barriers of distance and how the quality of surgical planning is enriched by a great sense of "being there", even if virtually. Further investigation will improve the quality of interaction with the models and among the avatars.

3D Model Artificial Intelligence-Guided Automatic Augmented Reality Images during Robotic Partial Nephrectomy.

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.

Artificial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3D®) Models in Surgical Planning of Challenging Robotic Nephron-Sparing Surgery: A Case Report and Snapshot of the State-of-the-Art with Possible Future Implications.

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.

New Generation of 3D Virtual Models with Perfusional Zones: Perioperative Assistance for the Best Pedicle Management during Robotic Partial Nephrectomy.

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.

Three-dimensional Virtual Models of the Kidney with Colored Perfusion Regions: A New Algorithm-based Tool for Optimizing the Clamping Strategy During Robot-assisted Partial Nephrectomy.

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.

Identification of Recurrent Anatomical Clusters Using Three-dimensional Virtual Models for Complex Renal Tumors with an Imperative Indication for Nephron-sparing Surgery: New Technological Tools for Driving Decision-making.

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.