Generative AI-Assisted Indication of Anatomical Landmarks to Enhance Safety under Intraoperative Bleeding in Laparoscopic Gastrectomy for Gastric Cancer.

Objective: This study aimed to present a novel approach that integrates a generative adversarial network (GAN) into the inference process of surgical navigation artificial intelligence (AI) systems. Background: Safe laparoscopic gastrectomy (LG) for gastric cancer relies on accurate identification of anatomical landmarks, such as the pancreas and dimpling lines (DLs). We developed a gastric cancer/gastrectomy AI (gAI) to indicate these landmarks. However, intraoperative bleeding often obscures the operative field, reducing the effectiveness of gAI. Methods: We created gAI-with-GAN by integrating a CycleGAN-based generative model into gAI to remove intraoperative bleeding during the inference process. We compared gAI-with-GAN with gAI using 30 LG videos categorized by bleeding severity. Expert surgeons evaluated the software usefulness, using a 5-point Likert scale and Dice coefficients. Results: In massive bleeding cases, gAI-with-GAN outperformed gAI. The median Likert scores for identifying the pancreas and the DL between the mesogastrium and pancreas improved from 3 to 4 ( Conclusions: Integrating generative AI improved the quality of anatomical landmark indications, even under intraoperative bleeding during LG. This approach may enhance surgical safety in minimally invasive surgeries.