## 3 Advancements in Imaging and Augmented Reality (AR)
### Augmented Reality in Intraoperative Navigation  
Augmented reality represents the next step in intraoperative image guidance. By superimposing 3D models onto the live surgical view from the endoscopic camera, AR enhances the surgeon's perception of structures hidden beneath the visible surface. AR has been employed during RAPN to project tumors, vessels, and collecting system anatomy directly onto the kidney, visible on the robotic console (Figure 3). This integration facilitates real-time orientation and navigation, minimizing the risk of intraoperative surprises. Intraoperative ultrasound, traditionally used for localization, has been shown to be less precise than AR-guided approaches in complex cases [13]. In a comparative study of patients with complex renal tumors (PADUA score \(\geq\) 10), [PERSON] et al. demonstrated that AR guidance using Hyper Accuracy(r) 3D (HA3D) models significantly improved intraoperative navigation during RAPN [13]. Compared to standard 2D ultrasound, AR guidance resulted in lower rates of global ischemia (45.8% vs. 69.7%, \(p\) = 0.03), higher tumor enucleation rates (62.5% vs. 37.5%, \(p\) = 0.02), and fewer collecting system violations (10.4% vs. 45.5%, \(p\) = 0.003). Postoperatively, AR was associated with a smaller drop in renal plasma flow at 3 months (-12.38 vs. -18.14, \(p\) = 0.01) and a lower rate of complications. These findings highlight the potential of AR to enhance surgical precision and improve functional outcomes in challenging renal tumor resectio  
Figure 2: A three-dimensional (3D) virtual model for surgical planning used to guide robotic partial nephrectomy. Supporting these findings, [PERSON] et al. showed that surgical navigation systems integrating real-time endoscopic views with 3D virtual models improved robotic efficiency during RAPN. Their 2019 study demonstrated that surgical navigation significantly reduced inefficient connected motions, such as \"insert', 'pull', and \"rotate\", leading to faster identification of the renal artery (9 vs. 16 min, \(p\) = 0.008) [14]. A second study by the same group, using propensity score-matched analysis, found that navigation-assisted RAPN achieved significantly greater parenchymal preservation (90.0% vs. 83.5%, \(p\) = 0.042) and better extraparenchymal resection control (21.4 mL vs. 17.2 mL, \(p\) = 0.041) [15]. A recent innovation in this field is the development of iKidney, an artificial intelligence-driven platform for automated AR overlay during RAPN [16]. [PERSON] et al. reported the first clinical use of this system, which leverages a convolutional neural network (ResNet-50) to segment the kidney from endoscopic images and align a 3D model with the in vivo anatomy in real time [16]. This eliminates the need for manual model manipulation by trained assistants, a major barrier to widespread adoption. In their case, the system enabled accurate identification and enucleoresection of a totally endophytic renal mass with selective clamping and no postoperative complications. Overlay precision reached 97.8% during model training, supporting its feasibility and clinical utility. The integration of AI into AR guidance may democratize this technology by making it more autonomous, scalable, and suitable for use even in non-tertiary centers. In RARP, AR has enabled the overlay of prostate tumors and extracapsular extension (ECE) zones onto the real-time surgical field, offering enhanced anatomical visualization during key steps, such as nerve-sparing and dissection. This has proven particularly useful for selective biopsies and intraoperative adjustment of the dissection plane. Some platforms have also introduced elastic 3D models, which deform in response to robotic manipulation, improving anatomical fidelity during surgery. [PERSON] et al. were among the first to explore AR integration into RARP using HA3D models based on mpMRI data [17].