Visual extended reality tools in image-guided surgery in urology: a systematic review.

PURPOSE: The aim of this systematic review is to assess the clinical implications of employing various Extended Reality (XR) tools for image guidance in urological surgery. METHODS: In June 2023, a systematic electronic literature search was conducted using the Medline database (via PubMed), Embase (via Ovid), Scopus, and Web of Science. The search strategy was designed based on the PICO (Patients, Intervention, Comparison, Outcome) criteria. Study protocol was registered on PROSPERO (registry number CRD42023449025). We incorporated retrospective and prospective comparative studies, along with single-arm studies, which provided information on the use of XR, Mixed Reality (MR), Augmented Reality (AR), and Virtual Reality (VR) in urological surgical procedures. Studies that were not written in English, non-original investigations, and those involving experimental research on animals or cadavers were excluded from our analysis. The quality assessment of comparative and cohort studies was conducted utilizing the Newcastle-Ottawa scale, whilst for randomized controlled trials (RCTs), the Jadad scale was adopted. The level of evidence for each study was determined based on the guidelines provided by the Oxford Centre for Evidence-Based Medicine. RESULTS: The initial electronic search yielded 1,803 papers after removing duplicates. Among these, 58 publications underwent a comprehensive review, leading to the inclusion of 40 studies that met the specified criteria for analysis. 11, 20 and 9 studies tested XR on prostate cancer, kidney cancer and miscellaneous, including bladder cancer and lithiasis surgeries, respectively. Focusing on the different technologies 20, 15 and 5 explored the potential of VR, AR and MR. The majority of the included studies (i.e., 22) were prospective non-randomized, whilst 7 and 11 were RCT and retrospective studies respectively. The included studies that revealed how these new tools can be useful both in preoperative and intraoperative setting for a tailored surgical approach. CONCLUSIONS: AR, VR and MR techniques have emerged as highly effective new tools for image-guided surgery, especially for urologic oncology. Nevertheless, the complete clinical advantages of these innovations are still in the process of evaluation.

Robotic partial nephrectomy in 3D virtual reconstructions era: is the paradigm changed?

CONTEXT: The development of a tailored, patient-specific medical and surgical approach is becoming object of intense research. In kidney oncologic surgery, where a clear understanding of case-specific surgical anatomy is considered a key point to optimize the perioperative outcomes, such philosophy gained increasing importance. Recently, important advances in 3D virtual modeling technologies have fueled the interest for their application in the field of robotic minimally invasive surgery for kidney tumors. OBJECTIVE: To provide a synthesis of current applications of 3D virtual models for robot-assisted partial nephrectomy. EVIDENCE ACQUISITION: Medline, PubMed, the Cochrane Database, and Embase were screened for Literature regarding the use of 3D virtual models for robot-assisted partial nephrectomy (RAPN). EVIDENCE SYNTHESIS: The use of 3D virtual models for RAPN has been tested in different settings, including surgical indication and planning, intraoperative guidance, and training. Currently, several studies are available on the application of this technology for surgical planning, demonstrating impact on clinical outcomes such as renal function recovery, whilst experiences concerning their intraoperative application for navigation are still experimental. One of the latest innovations in this field is represented by the development of dedicated softwares able to automatically overlap the 3D virtual models to the real anatomy, to perform augmented reality procedures. CONCLUSIONS: The available Literature suggests a potentially crucial role of 3D virtual reconstructions during RAPN. Encouraging results concerning surgical planning and indication, intraoperative navigation, and surgical training are available. In the future, artificial intelligence may represent the key to further improve the 3D virtual modeling technology during RAPN.

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.

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.

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.

Three-dimensional imaging reconstruction of the kidney's anatomy for a tailored minimally invasive partial nephrectomy: A pilot study.

Objective: The aim of the study was to evaluate three-dimensional virtual models (3DVMs) usefulness in the intraoperative assistance of minimally-invasive partial nephrectomy in highly complex renal tumors. Methods: At our institution cT1-2N0M0 all renal masses with Preoperative Aspects and Dimensions Used for an Anatomical classification score ≥10 treated with minimally-invasive partial nephrectomy were considered for the present study. For inclusion a baseline contrast-enhanced computed tomography in order to obtain 3DVMs, the baseline and postoperative serum creatinine as well as estimated glomerular filtration rate values were needed. These patients, in which 3DVMs were used to assist the surgeon in the planning and intraoperative guidance, were then compared with a control group of patients who underwent minimally-invasive partial nephrectomy with the same renal function assessments, but without 3DVMs. Multivariable logistic regression models were used to predict the margin, ischemia, and complication score achievement. Results: Overall, 79 patients met the inclusion criteria and were compared with 143 complex renal masses without 3DVM assistance. The 3DVM group showed better postoperative outcomes in terms of baseline-weighted differential estimated glomerular filtration rate (-17.7% vs. -22.2%, p=0.03), postoperative complications (16.5% vs. 23.1%, p=0.03), and major complications (Clavien Dindo >III, 2.5% vs. 5.6%, p=0.03). At multivariable logistic regression 3DVM assistance independently predicted higher rates of successful partial nephrectomy (odds ratio: 1.42, p=0.03). Conclusion: 3DVMs represent a useful tool to plan a tailored surgical approach in case of surgically complex masses. They can be used in different ways, matching the surgeon's needs from the planning phase to the demolitive and reconstructive phase, leading towards maximum safety and efficacy outcomes.