Anatomical recognition of dissection layers, nerves, vas deferens, and microvessels using artificial intelligence during transabdominal preperitoneal inguinal hernia repair

Purpose In laparoscopic inguinal hernia surgery, proper recognition of loose connective tissue, nerves, vas deferens, and microvessels is important to prevent postoperative complications, such as recurrence, pain, sexual dysfunction, and bleeding. EUREKA (Anaut Inc., Tokyo, Japan) is a system that uses artificial intelligence (AI) for anatomical recognition. This system can intraoperatively confirm the aforementioned anatomical landmarks. In this study, we validated the accuracy of EUREKA in recognizing dissection layers, nerves, vas deferens, and microvessels during transabdominal preperitoneal inguinal hernia repair (TAPP). Methods We used TAPP videos to compare EUREKA’s recognition of loose connective tissue, nerves, vas deferens, and microvessels with the original surgical video and examined whether EUREKA accurately identified these structures. Intersection over Union (IoU) and F1/Dice scores were calculated to quantitively evaluate AI predictive images. Results The mean IoU and F1/Dice scores were 0.33 and 0.50 for connective tissue, 0.24 and 0.38 for nerves, 0.50 and 0.66 for the vas deferens, and 0.30 and 0.45 for microvessels, respectively. Compared with the images without EUREKA visualization, dissection layers were very clearly recognized and displayed when appropriate tension was applied.