Enhancing anatomical recognition by surgeons during pelvic lymph node dissection using artificial intelligence

The lateral pelvis is a critical anatomical region in colorectal, gynecological, and urological surgeries. However, its anatomical complexity and variability pose significant challenges for pelvic lymph node dissection (PLND). This study aimed to develop an artificial intelligence (AI) model to identify key anatomical structures relevant to PLND and evaluate whether AI assistance enhances surgeons' ability to recognize pelvic anatomical features. Thirty-six surgeons representing colorectal, gynecological, and urological specialties, with varying experience levels, reviewed 640 video snippets (0.5 s each) from PLND procedures. The model was trained on 23,259 annotated and 653 unannotated images extracted from 293 PLND procedure videos. Threefold cross-validation yielded Dice similarity coefficients of 0.6483 for the ureter, 0.8654 for the obturator nerve, 0.8619 for the external iliac artery, and 0.8736 for the external iliac vein. Across all structures, AI assistance led to a significant improvement in sensitivity and specificity among participating surgeons (p < .001). Our findings suggest that the proposed AI model may assist surgeons in identifying pelvic anatomical structures across different specialties and experience levels. Further studies using continuous intraoperative workflows will be required to determine its impact on clinical practice.