OBJECTIVES
To evaluate the feasibility and accuracy of an artificial intelligence (AI) model to assist surgeons through automated real-time detection and segmentation of key anatomical structures during robot-assisted single-port transvesical enucleation of the prostate (STEP).
PATIENTS AND METHODS
This retrospective single-centre study utilised surgical videos from patients undergoing single-port robot-assisted transvesical prostate enucleation performed by a single expert surgeon. Selected frames extracted from these surgical videos were manually annotated to identify anatomical landmarks during the key step of the procedure. The structures annotated were the bladder neck, prostatic adenoma, and the peripheral zone. A convolutional neural network based on the You Only Look Once version 11 architecture was trained using these annotated frames. Model performance was quantitatively assessed through recall, precision, F1-score, Intersection over Union, Dice Similarity Coefficient, and mean average precision (AP). Real-time performance was assessed qualitatively through visual inspection and confirmed quantitatively by measuring frame inference speeds.
RESULTS
The study included 611 annotated frames derived from 37 surgical videos. The model demonstrated strong detection performance, with class-specific F1-scores of 0.88 (adenoma), 0.69 (bladder neck), and 0.70 (peripheral zone). Segmentation accuracy, measured by Dice Similarity Coefficient, resulted in adenoma: 0.86; bladder neck: 0.83; peripheral zone: 0.82. Mean AP across all anatomical classes was 0.43. The model consistently operated at >60 frames/s, confirming real-time applicability without perceptible lag.
CONCLUSIONS
This pilot study demonstrates the potential utility of AI to provide intraoperative anatomical guidance during STEP, establishing a foundation for future clinical integration and performance optimization.
L. Morgantini, Laura Cruciani, Andrea Lettieri et al.· BJU International· 0 citations
BACKGROUND
Real-time anatomical recognition during robot-assisted surgery has the potential to enrich intraoperative decision-making. We made the first clinical test of an artificial intelligence (AI)-based algorithm designed to identify key anatomical structures during robot-assisted retroperitoneal renal and adrenal procedures using the daVinci single-port (SP) platform.
METHODS
A deep learning algorithm, trained on annotated surgical video frames and retrained with an expanded dataset, was implemented to identify six anatomical structures: the psoas muscle, ureter, kidney, renal artery, renal vein, and inferior vena cava (IVC). The system's performance was prospectively evaluated in 15 patients operated on from April 2025 to July 2025, comparing the timing of recognition (Δ-time) with that of the operating surgeon, and recognition patterns (first, simultaneous, or exclusive identification) were recorded. The impact of patient body mass index (BMI) and history of previous abdominal surgery on recognition time were evaluated.
RESULTS
The surgeon was most frequently the first to recognize anatomical structures, including the psoas muscle (86.7%), renal artery (86.7%), and kidney (73.3%). The AI system achieved simultaneous recognition in a subset of cases but was rarely the first recognizer (psoas muscle 6.7%, renal vein 13.3%). No structures were identified exclusively by the AI, while several were recognized solely by the surgeon. Median Δ-time values ranged from 12.0 seconds (IVC) to 85.0 seconds (renal vein), with statistically significant differences for most structures (p < 0.01), except IVC (p = 0.100). In our analysis of recognition time, both BMI > 30 (p = 0.037) and a history of previous abdominal surgery (p = 0.019) were significantly associated with longer AI recognition time for the renal artery.
CONCLUSIONS
The AI algorithm demonstrated the capability to recognize key anatomical structures in real time, although the surgeon consistently outperformed it in terms of recognition timing. This tool shows promise as a supportive system for intraoperative guidance, particularly in enhancing situational awareness, with future improvements needed to reach autonomous-level recognition accuracy.
F. Tamborino, L. Morgantini, Laura Cruciani et al.· Journal of endourology· 0 citations
This study aimed to externally validate the performance of the European Association of Urology (EAU) Guidelines Bot in neuro-urology by assessing the accuracy, completeness, and clarity of chatbot-generated answers to guideline-based questions and to compare its performance with that of a general-purpose large language model (ChatGPT 5.5). A cross-sectional validation study was conducted using 47 questions derived from the EAU Neuro-Urology Guidelines. Each question was linked to a specific recommendation and classified by recommendation strength (strong vs weak). Questions were independently submitted to both the EAU Guidelines Bot and ChatGPT 5.5 without additional prompting. Two expert urologists independently evaluated each response for accuracy, completeness, and clarity using a five-point Likert scale; discrepancies were resolved by a third reviewer. Overall, 45 questions (95.7%) were linked to strong recommendations and two (4.3%) to weak recommendations. The EAU Guidelines Bot and ChatGPT 5.5 achieved identical mean accuracy scores (4.96 ± 0.20), with all responses rated as highly accurate (Likert 4-5). ChatGPT 5.5 indicated significantly higher completeness scores than did the EAU Guidelines Bot (4.74 ± 0.44 vs 4.57 ± 0.54; p = 0.011), whereas clarity scores were not significantly different (4.83 ± 0.38 vs 4.77 ± 0.43; p = 0.083). High-quality completeness was observed in 46/47 EAU Guidelines Bot responses (97.9%) and 47/47 ChatGPT responses (100%). Score discrepancies between systems were identified in ten of 47 questions (21.3%) and were limited to completeness and clarity domains. Performance remained uniformly high across recommendation grades, with no meaningful differences observed. The EAU Guidelines Bot showed excellent accuracy, completeness, and clarity when applied to neuro-urology guideline-based questions. Its performance was comparable to that of ChatGPT 5.5, with both systems providing highly accurate guideline-concordant responses. Although ChatGPT 5.5 generated more comprehensive answers, the EAU Guidelines Bot maintained closer adherence to the original guideline recommendations. Although not a substitute for clinical judgment, the tool appears to be a reliable adjunct for rapid access to evidence-based neuro-urological guidance.
Sabrina De Cillis, Riccardo Lombardo, Daniele Amparore et al.· European Urology Focus· 0 citations
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