Artificial intelligence for instance segmentation of MRI: advancing efficiency and safety in laparoscopic myomectomy of broad ligament fibroids - Liu, Feiran; Chen, Minghuang; Pan, Haixia; Li, Bin; Bai, Wenpei (Frontiers in Oncology, 2025)
BackgroundUterine broad ligament fibroids present unique surgical challenges due to their proximity to vital pelvic structures. This study aimed to evaluate artificial intelligence (AI)-guided MRI instance segmentation for optimizing laparoscopic myomectomy outcomes.MethodsIn this trial, 120 patients with MRI-confirmed broad ligament fibroids were allocated to either AI-assisted group (n=60) or conventional MRI group (n=60). A deep learning model was developed to segment fibroids, uterine walls, and uterine cavity from preoperative MRI.ResultCompared to conventional MRI guidance, AI assistance significantly reduced operative time (118 [112.25-125.00] vs. 140 [115.75-160.75] minutes; p<0.001). The AI group also demonstrated lower intraoperative blood loss (50 [50-100] vs. 85 [50-100] ml; p=0.01) and faster postoperative recovery (first flatus within 24 hours: (15[25.00%] vs. 29[48.33%], p=0.01).ConclusionThis multidisciplinary AI system enhances surgical precision through millimeter-level anatomical delineation, demonstrating transformative potential for complex gynecologic oncology procedures. Clinical adoption of this approach could reduce intraoperative blood loss and iatrogenic complications, thereby promoting postoperative recovery.