Three-dimensional Augmented Reality-guided Robotic-assisted Kidney Transplantation: Breaking the Limit of Atheromatic Plaques

Current status and future perspectives on robot-assisted kidney autotransplantation: A literature review

This review presents the latest insights on robot-assisted kidney autotransplantation (RAKAT). RAKAT is a minimally invasive surgical procedure and represents a promising alternative to conventional laparoscopic nephrectomy followed by open kidney transplantation for the treatment of various complex urological and vascular conditions. RAKAT can be performed either extracorporeally or intracorporeally. Additionally, a single-port approach can be performed through one small incision without the need to reposition the patient. Of 86 patients undergoing RAKAT, 8 (9.3%) developed postoperative > Grade 2 Clavien-Dindo (CD) complications. Although the feasibility of RAKAT was established in 2014, the long-term efficacy and safety along with outcomes of this surgical approach are still being evaluated, and additional studies are needed. With improvements in the technology of RAKAT and as surgeons gain more experience, RAKAT should become increasingly used and further refined, thereby leading to improved surgical outcomes and improved patients' quality of life.

Artificial Intelligence in Urologic Robotic Oncologic Surgery: A Narrative Review

With the rapid increase in computer processing capacity over the past two decades, machine learning techniques have been applied in many sectors of daily life. Machine learning in therapeutic settings is also gaining popularity. We analysed current studies on machine learning in robotic urologic surgery. We searched PubMed/Medline and Google Scholar up to December 2023. Search terms included "urologic surgery", "artificial intelligence", "machine learning", "neural network", "automation", and "robotic surgery". Automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction has been a major focus. Early artificial intelligence (AI) therapeutic outcomes are promising. Robot-assisted surgery provides precise telemetry data and a cutting-edge viewing console to analyse and improve AI integration in surgery. Machine learning enhances surgical skill feedback, procedure effectiveness, surgical guidance, and postoperative prediction. Tension-sensors on robotic arms and augmented reality can improve surgery. This provides real-time organ motion monitoring, improving precision and accuracy. As datasets develop and electronic health records are used more and more, these technologies will become more effective and useful. AI in robotic surgery is intended to improve surgical training and experience. Both seek precision to improve surgical care. AI in ''master-slave'' robotic surgery offers the detailed, step-by-step examination of autonomous robotic treatments.

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.

[Robot-assisted kidney transplantation-what is new?]

BACKGROUND

The first robot-assisted kidney transplantation (RAKT) was conducted in 2010, and the first time in Germany in 2016. As more than 5 years have passed, current evidence, technological developments and the latest (German) experience are presented.

OBJECTIVES

The current evidence and experience of RAKT was investigated from an international and German perspective.

MATERIALS AND METHODS

In a systemic search, relevant publications were analyzed and compared with the experiences at a German urological transplant department.

RESULTS

From an international perspective, RAKT can now be considered a standard procedure at experienced departments, as more than 680 RAKT have been documented in Europe. The functional results are excellent with low complication rates and good mid- to long-term functional outcomes. Although RAKT was initially only performed with living organ donations, it has also been successfully conducted with cadaveric grafts. The surgical technique can be applied in challenging and complex situations, such as for arteriosclerotic recipient vessels or for kidney transplantations in children. Although RAKT is still not widely performed in Germany, the university hospital in Marburg, the third urological department in Germany, has successfully initiated a robotic transplant program.

CONCLUSIONS

Compared to open kidney transplantation, robot-assisted kidney transplantation enables at least noninferior results. It further appears to translate the well-documented advantages of minimally invasive surgery to kidney transplantation. However, its spread throughout Germany is only slowly increasing, possibly because only a handful of urological departments still perform kidney transplantations.

Metaverse and Telementoring: From Surgery to Workshop

BACKGROUND

The Coronavirus 2019 (COVID-19) pandemic has favored the growth of telemedicine systems and in this context the idea of Metaverse was born and developed. A 3D reality in which people can interact with each other through digital reproductions of themselves. Metaverse has already been tested in numerous medical fields due to its ability to combine visual and auditory information with tactile sensations. The purpose of this study is to highlight its potential also in its ability to be used as a telementoring place where the skills and knowledge of surgeons from all over the world can be combined.

MATERIAL AND METHODS

The first HPB Surgery Workshop was held at the "Metaverse Surgical Hospital, USA". During the workshop, surgeons located in various parts of the world reported on hepatic, pancreatic and biliary tract surgery and remotely supported the execution of a robotic liver resection.

RESULTS

The Metaverse gave the opportunity for surgeons to meet and discuss HPB pathologies and its surgical strategies and for surgeons in training to interface with experts by participating in a moment of advanced training.

CONCLUSION

In the Metaverse, telementoring can be used at very low cost to improve clinical and surgical practice.

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.

Simulation training in urology

PURPOSE OF REVIEW

This review outlines recent innovations in simulation technology as it applies to urology. It is essential for the next generation of urologists to attain a solid foundation of technical and nontechnical skills, and simulation technology provides a variety of safe, controlled environments to acquire this baseline knowledge.

RECENT FINDINGS

With a focus on urology, this review first outlines the evidence to support surgical simulation, then discusses the strides being made in the development of 3D-printed models for surgical skill training and preoperative planning, virtual reality models for different urologic procedures, surgical skill assessment for simulation, and integration of simulation into urology residency curricula.

SUMMARY

Simulation continues to be an integral part of the journey towards the mastery of skills necessary for becoming an expert urologist. Clinicians and researchers should consider how to further incorporate simulation technology into residency training and help future generations of urologists throughout their career.

Kidney stones in renal transplant recipients: A systematic review

INTRODUCTION

Lithiasis in renal graft recipients might be a dangerous condition with a potential risk of organ function impairment.

EVIDENCE ACQUISITION

A systematic literature search was conducted through February 2023. The primary objective was to assess the incidence of lithiasis in kidney transplant (KT) recipients. The secondary objective was to assess the timing of stone formation, localization and composition of stones, possible treatment options, and the incidence of graft loss.

EVIDENCE SYNTHESIS

A total of 41 non-randomized studies comprising 699 patients met our inclusion criteria. The age at lithiasis diagnosis ranged between 29-53 years. Incidence of urolithiasis ranged from 0.1-6.3%, usually diagnosed after 12 months from KT. Most of the stones were diagnosed in the calyces or in the pelvis. Calcium oxalate composition was the most frequent. Different treatment strategies were considered, namely active surveillance, ureteroscopy, percutaneous/combined approach, or open surgery. 15.73% of patients were submitted to extracorporeal shock wave lithotripsy (ESWL), while 26.75% underwent endoscopic lithotripsy or stone extraction. 18.03% of patients underwent percutaneous nephrolithotomy whilst 3.14% to a combined approach. Surgical lithotomy was performed in 5.01% of the cases. Global stone-free rate was around 80%.

CONCLUSIONS

Lithiasis in kidney transplant is a rare condition usually diagnosed after one year after surgery and mostly located in the calyces and renal pelvis, more frequently of calcium oxalate composition. Each of the active treatments is associated with good results in terms of stone-free rate, thus the surgical technique should be chosen according to the patient's characteristics and surgeon preferences.

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.

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.

Can the Abdominal Aortic Atherosclerotic Plaque Index Predict Functional Outcomes after Robot-Assisted Partial Nephrectomy?

This study aims to evaluate the abdominal aortic atherosclerotic plaque index (API)'s predictive role in patients with pre-operatively or post-operatively developed chronic kidney disease (CKD) treated with robot-assisted partial nephrectomy (RAPN) for renal cell carcinoma (RCC). One hundred and eighty-three patients (134 with no pre- and post-operative CKD (no CKD) and 49 with persistent or post-operative CKD development (post-op CKD)) who underwent RAPN between January 2019 and January 2022 were deemed eligible for the analysis. The API was calculated using dedicated software by assessing the ratio between the CT scan atherosclerotic plaque volume and the abdominal aortic volume. The ROC regression model demonstrated the influence of API on CKD development, with an increasing effect according to its value (coefficient 0.13; 95% CI 0.04-0.23; p = 0.006). The Model 1 multivariable analysis of the predictors of post-op CKD found that the following are independently associated with post-op CKD: Charlson Comorbidity Index (OR 1.31; p = 0.01), last follow-up (FU) Δ%eGFR (OR 0.95; p < 0.01), and API ≥ 10 (OR 25.4; p = 0.01). Model 2 showed API ≥ 10 as the only factor associated with CKD development (OR 25.2; p = 0.04). The median follow-up was 22 months. Our results demonstrate API to be a strong predictor of post-operative CKD, allowing the surgeon to tailor the best treatment for each patient, especially in those who might be at higher risk of CKD.

Three-dimensional automatic artificial intelligence driven augmented-reality selective biopsy during nerve-sparing robot-assisted radical prostatectomy: A feasibility and accuracy study

Objective

To evaluate the accuracy of our new three-dimensional (3D) automatic augmented reality (AAR) system guided by artificial intelligence in the identification of tumour's location at the level of the preserved neurovascular bundle (NVB) at the end of the extirpative phase of nerve-sparing robot-assisted radical prostatectomy.

Methods

In this prospective study, we enrolled patients with prostate cancer (clinical stages cT1c-3, cN0, and cM0) with a positive index lesion at target biopsy, suspicious for capsular contact or extracapsular extension at preoperative multiparametric magnetic resonance imaging. Patients underwent robot-assisted radical prostatectomy at San Luigi Gonzaga Hospital (Orbassano, Turin, Italy), from December 2020 to December 2021. At the end of extirpative phase, thanks to our new AAR artificial intelligence driven system, the virtual prostate 3D model allowed to identify the tumour's location at the level of the preserved NVB and to perform a selective excisional biopsy, sparing the remaining portion of the bundle. Perioperative and postoperative data were evaluated, especially focusing on the positive surgical margin (PSM) rates, potency, continence recovery, and biochemical recurrence.

Results

Thirty-four patients were enrolled. In 15 (44.1%) cases, the target lesion was in contact with the prostatic capsule at multiparametric magnetic resonance imaging (Wheeler grade L2) while in 19 (55.9%) cases extracapsular extension was detected (Wheeler grade L3). 3D AAR guided biopsies were negative in all pathological tumour stage 2 (pT2) patients while they revealed the presence of cancer in 14 cases in the pT3 cohort (14/16; 87.5%). PSM rates were 0% and 7.1% in the pathological stages pT2 and pT3 (<3 mm, Gleason score 3), respectively.

Conclusion

With the proposed 3D AAR system, it is possible to correctly identify the lesion's location on the NVB in 87.5% of pT3 patients and perform a 3D-guided tailored nerve-sparing even in locally advanced diseases, without compromising the oncological safety in terms of PSM rates.

Improving Augmented Reality Through Deep Learning: Real-time Instrument Delineation in Robotic Renal Surgery

Several barriers prevent the integration and adoption of augmented reality (AR) in robotic renal surgery despite the increased availability of virtual three-dimensional (3D) models. Apart from correct model alignment and deformation, not all instruments are clearly visible in AR. Superimposition of a 3D model on top of the surgical stream, including the instruments, can result in a potentially hazardous surgical situation. We demonstrate real-time instrument detection during AR-guided robot-assisted partial nephrectomy and show the generalization of our algorithm to AR-guided robot-assisted kidney transplantation. We developed an algorithm using deep learning networks to detect all nonorganic items. This algorithm learned to extract this information for 65 927 manually labeled instruments on 15 100 frames. Our setup, which runs on a standalone laptop, was deployed in three different hospitals and used by four different surgeons. Instrument detection is a simple and feasible way to enhance the safety of AR-guided surgery. Future investigations should strive to optimize efficient video processing to minimize the 0.5-s delay currently experienced. General AR applications also need further optimization, including detection and tracking of organ deformation, for full clinical implementation.