Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2026
Generative AI-Assisted Indication of Anatomical Landmarks to Enhance Safety under Intraoperative Bleeding in Laparoscopic Gastrectomy for Gastric Cancer.
СтатьяНазвание и аннотация: Перевод в очереди
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Fujishima Reo, Matsunobu Yusuke, Etoh Tsuyoshi, Aoyama Yoshimasa, Hasegawa Takumi, Shitomi Yuki, Ninomiya Shigeo, Ueda Yoshitake, Hanada Reiko, Tokuyasu Tatsushi, Inomata Masafumi
Аннотация
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.
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.