Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery
Sang‐Ho Park, Hee-Min Park, Kwang‐Ryul Baek, Hong‐min Ahn, In Young Lee, Gyung Mo Son
Abstract
BACKGROUND: Colonic perfusion status can be assessed easily by indocyanine green (ICG) angiography to predict ischemia related anastomotic complications during laparoscopic colorectal surgery. Recently, various parameter-based perfusion analysis have been studied for quantitative evaluation, but the analysis results differ depending on the use of quantitative parameters due to differences in vascular anatomical structure. Therefore, it can help improve the accuracy and consistency by artificial intelligence (AI) based real-time analysis microperfusion (AIRAM). AIM: To evaluate the feasibility of AIRAM to predict the risk of anastomotic complication in the patient with laparoscopic colorectal cancer surgery. METHODS: = 15). Training ICG curve data sets were classified and machine learned into 25 ICG curve patterns using a self-organizing map (SOM) network. The predictive reliability of anastomotic complications in a trained SOM network is verified using test set. RESULTS: , 8% for TR, and 8% for RS. The processing time of AIRAM was measured as 48.03 s, which was suitable for real-time processing. CONCLUSION: In conclusion, AI-based real-time microcirculation analysis had more accurate and consistent performance than the conventional parameter-based method.