Generation of surgical reports for lymph node dissection during laparoscopic gastric cancer surgery based on artificial intelligence.

Abstract

Purpose: This study aimed to develop an artificial intelligence (AI) model for the surgical report output of laparoscopic lymph node dissection in the suprapancreatic region during gastric cancer surgery.

Methods: Patients who underwent laparoscopic radical resection for gastric cancer were included in this study, and their surgical videos were analyzed. The videos were recorded from the opening of the gastropancreatic fold as the starting point to the transection of the left gastric artery as the endpoint, with the video frame rate set to 1 frame per second. All surgical procedures were recorded following the principle of tool-tissue interaction, with annotations completed by an experienced surgeon and reviewed by a senior surgeon. The final annotated surgical videos were used as inputs for the AI model to generate the surgical report output.

Results: A total of 100 patients who underwent laparoscopic surgery for gastric cancer were included. A Surgical Concept Alignment Network was used as the model for surgical report output. The average number of frames in the videos was 728.71, with the grasping forceps being the most frequently used instrument. The AI model successfully generated a surgical video report output, achieving a BLEU-4 score of 0.7377, METEOR score of 0.4846, and ROUGE-L score of 0.7953.

Conclusion: The AI model demonstrates its capability in producing surgical report output for laparoscopic lymph node dissection in the suprapancreatic region during gastric cancer surgery. This model serves as a valuable tool in clinical diagnosis, treatment, and training.