Intraoperative real-time recognition of dissectible layers by artificial intelligence is useful in laparoscopic inguinal hernia repair

BACKGROUND: Accurate identification of anatomical landmarks such as loose connective tissue, nerves, the vas deferens, and microvessels is essential for transabdominal preperitoneal inguinal hernia repair (TAPP). However, this skill largely depends on the surgeon's experience. EUREKAα (Anaut Inc., Tokyo, Japan), an artificial intelligence (AI)-based surgical support system, was approved in Japan in April 2024 to provide real-time intraoperative recognition and visualization of loose connective tissue. METHODS: Of the 508 TAPP procedures performed between February 2020 and December 2025, 54 were carried out using real-time AI navigation (RAIN), whereas 454 were performed using conventional techniques. Patient characteristics, surgical outcomes, and postoperative complications were statistically analyzed to evaluate the effectiveness and safety of RAIN. Propensity score matching (PSM) was conducted. RESULTS: The RAIN group showed significantly shorter operative times (48.6 vs. 38.6 min) compared with the conventional group, with no significant differences in postoperative complications. After PSM, 53 matched pairs of patients for each group. Mean operative time (49.2 vs. 38.5 min) was significantly shorter in the RAIN group compared to the conventional group. CONCLUSIONS: Real-time intraoperative AI surgical support in TAPP was associated with shorter operative time without increasing postoperative complications. As the clinical use of EUREKAα is still in its early stages, its full value will be more clearly defined through future studies.