BJU international · 2026
Real-time artificial intelligence-based anatomy recognition in single-port transvesical enucleation of the prostate.
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Morgantini Luca A, Cruciani Laura, Lettieri Andrea Calzolai, Valenzi Fabio M, Santarelli Valerio, Tamborino Flavia, Turcan Alexandru, Carletti Filippo, Lughezzani Giovanni, Paciotti Marco, De Momi Elena, Buffi Nicolò M, Crivellaro Simone
Аннотация
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.
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.