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.

Development of Machine Learning Algorithm to Predict the Risk of Incontinence After Robot-Assisted Radical Prostatectomy.

Introduction: Predicting postoperative incontinence beforehand is crucial for intensified and personalized rehabilitation after robot-assisted radical prostatectomy. Although nomograms exist, their retrospective limitations highlight artificial intelligence (AI)'s potential. This study seeks to develop a machine learning algorithm using robot-assisted radical prostatectomy (RARP) data to predict postoperative incontinence, advancing personalized care. Materials and Methods: In this propsective observational study, patients with localized prostate cancer undergoing RARP between April 2022 and January 2023 were assessed. Preoperative variables included age, body mass index, prostate-specific antigen (PSA) levels, digital rectal examination (DRE) results, Gleason score, International Society of Urological Pathology grade, and continence and potency questionnaires responses. Intraoperative factors, postoperative outcomes, and pathological variables were recorded. Urinary continence was evaluated using the Expanded Prostate cancer Index Composite questionnaire, and machine learning models (XGBoost, Random Forest, Logistic Regression) were explored to predict incontinence risk. The chosen model's SHAP values elucidated variables impacting predictions. Results: A dataset of 227 patients undergoing RARP was considered for the study. Post-RARP complications were predominantly low grade, and urinary continence rates were 74.2%, 80.7%, and 91.4% at 7, 13, and 90 days after catheter removal, respectively. Employing machine learning, XGBoost proved the most effective in predicting postoperative incontinence risk. Significant variables identified by the algorithm included nerve-sparing approach, age, DRE, and total PSA. The model's threshold of 0.67 categorized patients into high or low risk, offering personalized predictions about the risk of incontinence after surgery. Conclusions: Predicting postoperative incontinence is crucial for tailoring rehabilitation after RARP. Machine learning algorithm, particularly XGBoost, can effectively identify those variables more heavily, impacting the outcome of postoperative continence, allowing to build an AI-driven model addressing the current challenges in post-RARP rehabilitation.

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.

Development of Bleeding Artificial Intelligence Detector (BLAIR) System for Robotic Radical Prostatectomy.

BACKGROUND: Addressing intraoperative bleeding remains a significant challenge in the field of robotic surgery. This research endeavors to pioneer a groundbreaking solution utilizing convolutional neural networks (CNNs). The objective is to establish a system capable of forecasting instances of intraoperative bleeding during robot-assisted radical prostatectomy (RARP) and promptly notify the surgeon about bleeding risks. METHODS: To achieve this, a multi-task learning (MTL) CNN was introduced, leveraging a modified version of the U-Net architecture. The aim was to categorize video input as either "absence of blood accumulation" (0) or "presence of blood accumulation" (1). To facilitate seamless interaction with the neural networks, the Bleeding Artificial Intelligence-based Detector (BLAIR) software was created using the Python Keras API and built upon the PyQT framework. A subsequent clinical assessment of BLAIR's efficacy was performed, comparing its bleeding identification performance against that of a urologist. Various perioperative variables were also gathered. For optimal MTL-CNN training parameterization, a multi-task loss function was adopted to enhance the accuracy of event detection by taking advantage of surgical tools' semantic segmentation. Additionally, the Multiple Correspondence Analysis (MCA) approach was employed to assess software performance. RESULTS: The MTL-CNN demonstrated a remarkable event recognition accuracy of 90.63%. When evaluating BLAIR's predictive ability and its capacity to pre-warn surgeons of potential bleeding incidents, the density plot highlighted a striking similarity between BLAIR and human assessments. In fact, BLAIR exhibited a faster response. Notably, the MCA analysis revealed no discernible distinction between the software and human performance in accurately identifying instances of bleeding. CONCLUSION: The BLAIR software proved its competence by achieving over 90% accuracy in predicting bleeding events during RARP. This accomplishment underscores the potential of AI to assist surgeons during interventions. This study exemplifies the positive impact AI applications can have on surgical procedures.

Initial experience with robot-assisted ureteroscopy with Ily® Robot.

The aim of this study is to present the first Italian experience with robotic-assisted retrograde intrarenal surgery (rRIRS) using the Ily® platform. Procedures were performed for renal stones using the Ily® Robot (STERLAB, Vallauris, France), which is a ureteroscope holder with multiple degrees of freedom that can be controlled remotely through a wireless controller. In March 2023, consecutive patients with indications for rRIRS were included in the study. Demographic variables and stone characteristics were collected, and standard perioperative data were assessed. The one-month stone-free rate (SFR, i.e. no residual fragments) was evaluated using ultrasound. All participating surgeons filled out a Surgeons' Satisfaction Questionnaire (SSQ) based on a Likert-type scale. The questionnaire focused on: 1) ease of use; 2) ergonomics during renal cavity exploration; 3) stability during stone fragmentation. Among the patients, one had bilateral stones, while two had stones on the right side. The mean stone size was 13 mm. The average operative time was 70 minutes and the mean docking time was three minutes. No perioperative complications were recorded, and all patients were discharged on the first postoperative day. The one-month SFR was 100%. The SSQ scores were as follows: 1) ease of use: 4/5; 2) ergonomics: 5/5; 3) stability during stone fragmentation: 5/5. Based on the initial experience, the results indicate the feasibility, safety, and effectiveness of rRIRS. The ergonomic efficiency of the system was highly appreciated by the surgeons. While a cost-effectiveness analysis within clinical trials is necessary, rRIRS shows the potential for a more sustainable future for endoscopists and an improved workplace environment.

Comparison of perioperative and short-terms outcomes of en-bloc Holmium laser enucleation of the prostate (HoLEP) and robot-assisted simple prostatectomy: a propensity-score matching analysis.

BACKGROUND: Surgical treatment of symptomatic benign prostatic hyperplasia (BPH) has seen an evolution during the last decades. On one hand, en-bloc HoLEP emerged as a valid endoscopic treatment regardless prostate size. On the other hand, robot-assisted simple prostatectomy (RASP) has gained attention in larger prostates showing encouraging results. Herein, for the first time in the scientific scenario, we sought to compare the outcomes of RASP and en-bloc HoLEP cases after propensity-score matching (PSM) analysis. METHODS: We retrospectively queried our prospectively database of patients treated with HoLEP or RASP between 2017 and 2022 among two high-volume centers. PSM was applied based on the International Prostate Symptom Score (IPSS) questionnaire, prostate volume and max-flow rate. All procedures were performed by a single surgeon per center. Outcomes were assessed at 1, 3, and 6-month postoperatively and therefore annually. Trifecta definition was used to assess "success" in surgical procedures and was defined as the contemporary presence of: a) no postoperative complications within the first postoperative month; b) 1-month postoperative Qmax >15 ml/s and c) no urinary incontinence at 3-month evaluation. RESULTS: Overall, 48 HoLEP and 47 RASPs were matched. Operative time, hospitalization time (median 4 vs 5 days) and catheterization time (median 3 vs 2 day) were found to be shorter in the HoLEP group as compared to the counterpart (p < 0.05). Early postoperative complication rate was also lower in the HoLEP cohort (6.2% vs 12.6%; p = 0.03) as well as postoperative haemoglobine blood level drop (1.4 vs 2.4 g/dL; p = 0.03). On the other hand, postoperative antegrade ejaculation (55.3% vs 6.8%) 1-month max flow (median 28 vs 24 ml/sec) and continence rates (0% vs 20.8%) favored RASP (p < 0.05). Overall, Trifecta rate was similar in the two groups (76.1% vs 82.6%). CONCLUSION: Both HoLEP and RASP are safe and effective treatments for symptomatic BPH. HoLEP demonstrated to have lower perioperative risks while is affecting by a higher probability of transient early UI. On the other hand, RASP is more effective in reducing postoperative ejaculatory dysfunction.

Predictors of mid-term functional outcomes for robot-assisted Madigan simple prostatectomy: results of a multicentric series according to the BPH-6 achievement.

BACKGROUND: BPH-6 achievement remains an objective far to be evaluated for every technique currently available for the surgical management of bladder outlet obstruction (BOO) with the goal of preserving ejaculatory function. The aim of this study was to evaluate predictors of BPH-6 achievement of urethral-sparing robot assisted simple prostatectomy (us-RASP) on a large series performed at two tertiary-care centers. METHODS: Two institutional us-RASP datasets were merged, considering eligible all patients with a follow-up >12 months. Baseline, perioperative and functional data according to BPH-6 endpoint were assessed. Descriptive analysis was used. Frequencies and proportions were reported for categorical variables while medians and interquartile ranges (IQRs) were reported for continuously coded variables. A logistic regression model was built to identify predictors of BPH-6 achievement. For all statistical analyses, a two-sided P<0.05 was considered significant. RESULTS: Study cohort consisted of 94 eligible patients. The median follow-up was 40.7 months (IQR 31.3-54.2). Overall BPH-6 achievement was 54.7%. Compared to baseline, reduction of ≥30% in IPSS was observed in 93.6% of patients, reduction of <6 points for SHIM in 95.7% and response to MSHQ-EjD question 3 indicating emission of semen in 72.6%, respectively. On multivariable analysis, prostate volume between 110-180 mL (OR 0.09; 95% CI 0.01-0.92; P=0.043) and higher preoperative SHIM score (OR 1.18; 95% CI 1.05-1.32; P<0.01) were independent predictors of BPH-6 metric achievement. CONCLUSIONS: us-RASP may provide a complete resolution of BOO and preservation of ejaculatory function in sexually active men with a prostate volume ranging 110-180 mL.