ArticleSurgical endoscopy2024
Artificial intelligence for surgical safety during laparoscopic gastrectomy for gastric cancer: Indication of anatomical landmarks related to postoperative pancreatic fistula using deep learning.
Article in Surgical endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- First-in-human evaluation of real-time pixel-level AI-assisted anatomical segmentation in neurosurgery: pituitary surgery as an exemplar.Pituitary · 2026Article
- AI-based assessment of surgical efficiency and exposure quality in laparoscopic distal gastrectomy using a phase recognition model.Surgical endoscopy · 2026Article
- Impact of artificial intelligence intervention on awareness of anatomical landmarks in laparoscopic gastrectomy: An exploratory study.Surgical endoscopy · 2026Article
- Artificial intelligence-based anatomical recognition improves surgeon decision-making during robotic gastrectomy.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- Generative AI-Assisted Indication of Anatomical Landmarks to Enhance Safety under Intraoperative Bleeding in Laparoscopic Gastrectomy for Gastric Cancer.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2026Article
- Educational Impact of Artificial Intelligence-Navigation Surgery on Anatomical Landmark Recognition in Medical Students.Annals of gastroenterological surgery · 2026Article
- Artificial intelligence-assisted risk prediction of postoperative pulmonary complications in non-small cell lung cancer surgery.Journal of cardiothoracic surgery · 2026Article
- Real-time multicenter semantic segmentation for laparoscopic gastrectomy: development, external validation and operating room deployment of an AI navigation system.Military Medical Research · 2026Article
- Artificial intelligence-assisted scar visualization under intraoperative bleeding using CycleGAN and uncertainty fusion in laparoscopic cholecystectomy.Surgical endoscopy · 2025Article
- Assessment of AI-driven anatomical recognition in robotic gastrectomy: A multicenter retrospective analysis.Surgical endoscopy · 2025Article
- Challenges in clinical translation of artificial intelligence and real-time imaging navigation in radical gastrectomy.World journal of gastroenterology · 2025Review
- Effects of enhanced recovery after surgery on postoperative intestinal function and intestinal flora during laparoscopic gastric cancer surgery.World journal of gastrointestinal surgery · 2025Article
- Preoperative interleukin-17a as a predictor of acute pancreatitis after pancreaticoduodenectomy.World journal of gastrointestinal surgery · 2025Article
- The application of artificial intelligence in upper gastrointestinal cancers.Journal of the National Cancer Center · 2025Review
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
18 authors.
Funding
Abstract
backgroundPostoperative pancreatic fistula (POPF) is a critical complication of laparoscopic gastrectomy (LG). However, there are no widely recognized anatomical landmarks to prevent POPF during LG. This study aimed to identify anatomical landmarks related to POPF occurrence during LG for gastric cancer and to develop an artificial intelligence (AI) navigation system for indicating these landmarks.
methodsDimpling lines (DLs)-depressions formed between the pancreas and surrounding organs-were defined as anatomical landmarks related to POPF. The DLs for the mesogastrium, intestine, and transverse mesocolon were named DMP, DIP, and DTP, respectively. We included 50 LG cases to develop the AI system (45/50 were used for training and 5/50 for adjusting the hyperparameters of the employed system). Regarding the validation of the AI system, DLs were assessed by an external evaluation committee using a Likert scale, and the pancreas was assessed using the Dice coefficient, with 10 prospectively registered cases.
resultsSix expert surgeons confirmed the efficacy of DLs as anatomical landmarks related to POPF in LG. An AI system was developed using a semantic segmentation model that indicated DLs in real-time when this system was synchronized during surgery. Additionally, the distribution of scores for DMP was significantly higher than that of the other DLs (p < 0.001), indicating the relatively high accuracy of this landmark. In addition, the Dice coefficient of the pancreas was 0.70.
conclusionsThe DLs may be used as anatomical landmarks related to POPF occurrence. The developed AI navigation system can help visualize the DLs in real-time during LG.
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