## 4 Real-Time Tissue Recognition and Margin Assessment
### In Vivo Optical and Spectroscopic Techniques  
In contrast to ex vivo tools, several emerging technologies now offer the ability to assess tissue intraoperatively while it remains in situ. These optical and spectroscopic methods aim to provide real-time guidance during robot-assisted surgery, particularly in procedures requiring precise margin control and nerve preservation. One such technique is confocal laser endomicroscopy (CLE), a fiber-optic imaging technology that provides real-time, high-resolution visualization of tissues at the cellular level, using a 488-nm laser in conjunction with intravenously administered fluorescein. Originally applied in urology for upper tract and bladder tumors, CLE has also been investigated for intraoperative use in RARP. In a feasibility study by [PERSON] et al., CLE was successfully integrated into the da Vinci robotic platform using the TilePro(tm) interface, allowing direct visualization of the prostatic and periprostatic tissues during nerve-sparing dissection [26]. The system employs miniature probes, ranging from 0.85 to 2.6 mm in diameter, offering varying fields of view and resolution depending on the surgical access requirements. Key anatomical landmarks, including the bladder neck, urethral stump, and neurovascular bundles, were clearly visualized, suggesting that CLE could serve as a real-time adjunct to pathological assessment. To support clinical interpretation, a visual atlas of prostate-specific CLE images has been developed [27]. While promising, clinical data remain limited, and larger studies are needed to determine whether CLE can reliably guide margin assessment and improve functional outcomes in prostate cancer surgery.  
Raman spectroscopy (RS) is another promising modality, utilizing inelastic light scattering to differentiate tissue types based on molecular composition. When applied intraoperatively through fiber-optic probes, RS has demonstrated high sensitivity (approximately 87%) and specificity (around 86%) in distinguishing benign from malignant prostate tissue [28]. A notable advancement in this field is the integration of RS directly into the da Vinci robotic surgical platform, as demonstrated by [PERSON] et al. They developed a dual-wavelength RS system (680 and 785 nm) adapted for robotic manipulation, and successfully collected in vivo spectra at the surgical margin during RARP. In a proof-of-concept study involving 20 prostatectomy specimens and additional intraoperative measurements in four patients, RS achieved impressive accuracy (91%), sensitivity (90.5%), and specificity (96%) in distinguishing prostatic from extraprostatic tissues [29]. The system's compatibility with standard robotic instruments and fast acquisition time (-2 s per point) suggests it can be incorporated into surgical workflows with minimal disruption. These early results highlight the potential of RS to serve as a real-time margin assessment tool, potentially reducing positive surgical margins and supporting nerve-sparing decisions. However, broader clinical validation, including robust classification models and standardized workflows, is needed before routine adoption in robotic prostate surgery.  
Key variables, such as sensitivity, specificity, cost-effectiveness, and ease of use, differ significantly, suggesting that a single \"best\" tool may not be appropriate across all clinical contexts. Despite strong early-phase data, these technologies must overcome barriers including capital cost, training requirements, and lack of reimbursement pathways. Regulatory pathways and collaborative trials are necessary to establish their value in oncologic outcomes and patient-centered metrics.