Artificial intelligence-driven 3-dimensional simulation system for enhanced preoperative planning in gastric cancer surgery: a retrospective validation study.

BACKGROUND

Few studies have developed artificial intelligence (AI) systems for the automatic recognition of the anatomy of the stomach, a dynamic organ capable of expansion and contraction. This study aimed to create a 3-dimensional (3D) simulation to assist gastric cancer surgery by combining AI models to visualize the positional relationships among the stomach, surrounding organs, and blood vessels.

METHODS

A deep learning-based model was developed using an AI system to segment abdominal organs and detect blood vessels, including midartery-level structures, from contrast-enhanced computed tomography (CT) images. Surgical structures, including the stomach, pancreas, and arteries, were extracted using a blood vessel detection model. Of note, 2 surgeons and 2 radiologists evaluated 51 3D images for structural detection confidence using a 5-point scale and compared them to standard CT images.

RESULTS

A retrospective analysis of 51 cases of preoperative patients with gastric cancer demonstrated that AI-generated images provided clear visualization of the spatial relationships between blood vessels and organs. Structures, including the left hepatic-left gastric artery, common duct and its branches, and the short gastric artery distinct from the splenic artery, were clearly identified. These findings were useful for surgical planning. The reliability score for detecting blood vessels was significantly higher (P <.05) for the AI images than for the CT images, with good agreement among the evaluators.

CONCLUSION

Automatic organ recognition systems are promising, valuable tools for gastric cancer surgery, improving preoperative planning and potentially reducing operative time and complications.