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.

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.

Introduction: Penile cancer (PC) is a rare malignancy with an overall incidence in Europe of 1/100,000 males/year. In Europe, few studies report the epidemiology, risk factors, clinical presentation, and treatment of PC. The aim of this study is to present an updated outlook on the aforementioned factors of PC in Spain. Materials and Methods: A multicentric, retrospective, observational epidemiological study was designed, and patients with a new diagnosis of PC in 2015 were included. Patients were anonymously identified from the Register of Specialized Care Activity of the Ministry of Health of Spain. All Spanish hospitals recruiting patients in 2015 were invited to participate in the present study. We have followed a descriptive narration of the observed data. Continuous and categorical data were reported by median (p25th–p75th range) and absolute and relative frequencies, respectively. The incidence map shows differences between Spanish regions. Results: The incidence of PC in Spain in 2015 was 2.55/100,000 males per year. A total of 586 patients were identified, and 228 patients from 61 hospitals were included in the analysis. A total of 54/61 (88.5%) centers reported ≤ 5 new cases. The patients accessed the urologist for visually-assessed penile lesions (60.5%), mainly localized in the glans (63.6%). Local hygiene, smoking habits, sexual habits, HPV exposure, and history of penile lesions were reported in 48.2%, 59.6%, 25%, 13.2%, and 69.7%. HPV-positive lesions were 18.1% (28.6% HPV-16). The majority of PC was squamous carcinoma (95.2%). PC was ≥cT2 in 45.2% (103/228) cases. At final pathology, PC was ≥pT2 in 51% of patients and ≥pN1 in 17% of cases. The most common local treatment was partial penectomy (46.9% cases). A total of 47/55 (85.5%) inguinal lymphadenectomies were open. Patients with ≥pN1 disease were treated with chemotherapy in 12/39 (40.8%) of cases. Conclusions: PC incidence is relatively high in Spain compared to other European countries. The risk factors for PC are usually misreported. The diagnosis and management of PC are suboptimal, encouraging the identification of referral centers for PC management.

Objectives: Many patients with upper tract urothelial carcinoma (UTUC) outside of the low-risk criteria may possess low absolute risks of distant progression. Herein, we hypothesized that careful selection of high-risk patients undergoing an endoscopic approach could result in acceptable oncologic outcomes. Materials and Methods: Patients with high-risk UTUC managed endoscopically between 2015 and 2021 were retrospectively identified from a prospectively maintained single academic institution database. Elective and imperative indications for endoscopic treatment were considered. Regarding elective indications, the decision to perform endoscopic treatment was systematically proposed to high-risk patients in whom macroscopically complete ablation was deemed feasible, excluding invasive appearance on CT scan, and without histologic variant. Results: A total of 60 patients with high-risk UTUC met our inclusion criteria (29 imperative and 31 elective indications). The median follow-up in patients without any event was 36 months. At 5 years, the estimated overall survival, cancer-specific survival, metastasis-free survival, UTUC recurrence-free survival, radical nephroureterectomy-free survival, and bladder recurrence-free survival were 57% (41–79), 75% (57–99), 86% (71–100), 56% (40–76), 81% (70–93), and 69% (54–88), respectively. All oncologic outcomes were similar between patients with elective and imperative indications (all log-rank p > 0.05). Conclusions: In conclusion, we report the first large series of endoscopic treatment in patients with high-risk UTUC, arguing that promising oncologic outcomes can be achieved in properly selected candidates. We encourage multi-institutional collaborative work as a large cohort of high-risk patients treated endoscopically may allow subgroup analyses to define the best candidates.

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.