AI-driven automatic compression system for colorectal anastomosis.

Abstract

Although circular staplers offer technical advancements over traditional hand-sewn techniques, their use remains challenging for unskilled users, necessitating substantial time and experience for mastery. In particular, it is challenging to apply a consistent pressure of an appropriate magnitude. We developed an automated circular anastomosis device using artificial intelligence (AI) to solve this problem. Automation through AI reduces experiential factors during the anastomosis process. We defined damage occurring during the anastomosis process, noting that a greater depth of damage indicated a more severe injury. For automated anastomosis, data at a tissue strain of 40% were used for the AI model, as this strain level showed optimal performance based on the accuracy and cost matrix. We compared the outcomes of automated anastomosis using a trained AI with those of unskilled users. The results were validated using the Shapiro-Wilk test and t tests. Compression damage was verified on collagen sheets. The AI-driven automatic compression system resulted in less damage compared to unskilled users. In particular, a more significant difference in damage was observed in poor-condition collagen than in good-condition collagen. Damage to the collagen under poor conditions was 54.8% when handled by unskilled users, while the AI-driven automatic compression system resulted in 38.9% damage. This study confirmed that novices' use of AI for automated anastomosis reduces the risk of damage, especially for tissues in poor condition.