## 4 Real-Time Tissue Recognition and Margin Assessment
### Augmented Reality and Artificial Intelligence for Margin Guidance  
Emerging technologies, such as augmented reality (AR) and artificial intelligence (AI), are reshaping intraoperative strategies for margin assessment. AR platforms that overlay MRI-derived 3D models onto live endoscopic views provide surgeons with a non-invasive method to visualize areas at increased risk of extracapsular extension [34]. Though not direct detection tools, these systems enhance spatial orientation and can guide selective biopsies or dynamic adjustments to nerve-sparing plans. Advanced AR systems also simulate soft-tissue deformation, improving the anatomical accuracy of overlaid tumor boundaries during real-time manipulation [19]. In parallel, machine learning algorithms are being developed to interpret intraoperative imaging modalities with increasing autonomy. Automated segmentation of PSMA PET-CT data has already demonstrated high accuracy (up to 97-99%) in correlating radiologic signals with histopathologic findings [35]. A summary of AR and real-time margin assessment technologies can be found in Table 1. A comparative analysis of biosensor technologies can be found in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology** & **Application** & **Clinical Validation** & **Reference** \\ \hline Hyperaccuracy 3D AR for RAPN & \begin{tabular}{c} Navigation and tumor \\ enucleation \\ \end{tabular} & \begin{tabular}{c} Improved functional outcomes, \\ reduced ischemia \\ \end{tabular} & [PERSON] et al. (2020) [13] \\ \hline Elastic AR for RARP & \begin{tabular}{c} Capsular involvement \\ identification \\ \end{tabular} & 100\% capsular accuracy vs. 47\% & [PERSON] et al. (2019) [19] \\ \hline iKidney AI-AR System & Automated AR alignment & \begin{tabular}{c} First clinical use reported, 97.8\% \\ overlay precision \\ \end{tabular} & [PERSON] et al. (2023) [16] \\ \hline Confocal Laser Endomicroscopy (CLE) & \begin{tabular}{c} In vivo nerve-sparing \\ visualization \\ \end{tabular} & \begin{tabular}{c} Feasibility study; high-quality \\ imaging of landmarks \\ \end{tabular} & [PERSON] et al. (2016) [26] \\ \hline Fluorescence Confocal Microscopy (FCM) & \begin{tabular}{c} Ex vivo prostate margin \\ evaluation \\ \end{tabular} & \begin{tabular}{c} >90\% accuracy; rapid margin \\ assessment \\ \end{tabular} & [PERSON] et al. (2019); [PERSON] et al. (2021) [23; 24] \\ \hline Raman Spectroscopy (RS) & In vivo tissue differentiation & 91\% accuracy in vivo; pilot use in & [PERSON] et al. (2019) [29] \\ \hline Indocyanine Green (ICG) with NIRF Imaging & \
\begin{tabular}{c} Perfusion and tumor contrast in \\ RAPN \\ \end{tabular} &
\begin{tabular}{c} Widely used; validated in \\ numerous RAPN studies \\ \end{tabular} & [PERSON] et al. (2020); [PERSON] et al. (2013) [32; 33] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of AR and Margin Assessment Technologies in Robotic Urologic Oncology.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Technology** & **Sensitivity** & **Specificity** & **Cost-Effectiveness** & **Ease of Use** & **Reference** \\ \hline Confocal Enomicroscopy (CLE) & High & High & Moderate & Moderate & [PERSON] et al. (2016) [26] \\ \hline Fluorescence Confocal Microscopy (FCM) & \textgreater{}90\% & \textgreater{}90\% & Moderate to High & High (ex vivo) & [PERSON] et al. (2019); [PERSON] et al. (2021) [23; 24] \\ \hline Raman Spectroscopy (RS) & \textgreater{}91\% & \textgreater{}96\% & High & Moderate (requires training) & [PERSON] et al. (2019) [29] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative Analysis of Biosensor Technologies.