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
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