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

Abstract

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 ( P < 0.05). Scores for the accuracy of pancreatic indication improved from 2 to 3 ( P < 0.05), and scores for potential clinical utility in preventing postoperative pancreatic fistula increased from 2 to 4 ( P < 0.005). Dice coefficients for the pancreas increased from 0.36 ± 0.26 to 0.65 ± 0.17 ( P < 0.05). 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.

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