Journal of endourology · 2026
Real-Time Anatomy Recognition During Single-Port Retroperitoneal Surgery: A Prospective Comparison Between AI and Operating Surgeon.
СтатьяНазвание и аннотация: Перевод в очереди
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Tamborino Flavia, Morgantini Luca A, Cruciani Laura, Turcan Alexandru, Carletti Filippo, Valenzi Fabio Maria, Santarelli Valerio, Haberal Hakan Bahadir, Calzolai Lettieri Andrea, Schiavina Leonardo, Schips Luigi, Crivellaro Simone
Аннотация
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.
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.