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.

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.

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.

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.

(1) Background: We report a very unusual presentation of pseudoaneurysm developed following a robot-assisted radical prostatectomy (RARP) with bilateral pelvic lymph node dissection. The patient was a 66-year-old male, admitted at the Emergency Department 20 days after discharge from RARP due to chest pain, lipothymia and occult bleeding, necessitating a blood transfusion and a moderate increase of serum troponin. Angiography showed a pseudo-aneurysm involving the inferior-epigastric artery pubic branch. The patient underwent percutaneous embolization with complete resolution. The aim of this study is to offer an overview on pseudoaneurysms developed as radical prostatectomy complications; (2) Methods: A literature search was conducted on Scopus and PubMed for pseudoaneurysms after RARP to analyze all the previously published cases. The search terms used were “pseudoaneurysm” and “prostatectomy” using the Boolean Operator “AND”. We used the default search strategy “all fields”; (3) Results: Herein, a narrative review is proposed to facilitate the approach to this emerging clinical challenge, due to both an implementing in diagnosis imaging techniques and a spread of robotic surgery in prostate cancer; (4) Conclusions: The management of radical prostatectomy complications still remains barely uniformed and needs to be precisely calibrated as the initial cure approach itself.

Background: Nowadays, thanks to the introduction of the VI-RADS scoring system, mpMRI has shown promising results in pre-TURBT assessment of muscular invasiveness of BCa, even if its application in everyday practice is still limited. This might be due to a lack in the literature about the learning curve of radiologists and about the characteristics of the exam. With the aim to reduce scan time and patient discomfort while maintaining diagnostic accuracy, bpMRI has been introduced as a possible alternative to mpMRI in this group of patients. This study reports a single-center experience using mpMRI and the VI-RADS scoring system to differentiate NMIBC from MIBC. The primary aim of the study is to assess diagnostic accuracy of mpMRI using the VI-RADS scoring system. The secondary aim is to evaluate the learning curve of an experienced mpMRI radiologist. Additionally, we perform a retrospective assessment of the same group of patients evaluating only DWIs and T2-weighted images, as they underwent bpMRI, to compare the performance of mpMRI and bpMRI. Materials and Methods: From 11/2021 to 11/2023, patients with suspected newly diagnosed BCa were enrolled in this prospective study. All patients underwent mpMRI prior to TURBT in a highly specialized radiology center for MRI. According to VI-RADS, a cutoff of ≥3 was assumed to define MIBC. Histological TURBT reports were compared with preoperative VI-RADS scores to assess the accuracy of mpMRI in discriminating between NMIBC and MIBC. Furthermore, to assess the learning curve of the reading radiologist we analyzed the rate of patients correctly classified as MIBC at MRI. Finally, we evaluated the performance of a hypothetic biparametric MRI in classifying our cohort according to VI-RADS score and compared it with mpMRI performance by using DeLong’s test. Data analysis was performed using Jamovi software v.2.3 and R software v.4.2.1. Results: A total of 133 patients were enrolled. mpMRI showed sensitivity and specificity of 86% (95% confidence interval [CI]: 64–97) and 95% (95% CI: 89–98), respectively. The learning curve analysis of the reading radiologist showed that the rate of patients correctly classified as MIBC rapidly increases reaching its plateau after 40 cases. The hypothetic bpMRI showed a sensitivity of 76% (95% CI: 53–92) and a specificity of 93% (95% CI: 86–97), with no significant difference with mpMRI performance (p = 0.10). Conclusions: Our study confirms the effectiveness of MRI, particularly with the VI-RADS scoring system, in differentiating NMIBC from MIBC. The learning curve analysis underscores the importance of radiologist training in optimizing diagnostic accuracy. Future research should focus on enhancing the sensitivity of bpMRI and further validating these findings in larger and multicentric studies.