摘要
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 The ICG curve was extracted from the region of interest(ROI)set in the ICG fluorescence video of the laparoscopic colorectal surgery.Pre-processing was performed to reduce AI performance degradation caused by external environment such as background,light source reflection,and camera shaking using MATLAB 2019 on an I7-8700k Intel central processing unit(CPU)PC.AI learning and evaluation were performed by dividing into a training patient group(n=50)and a test patient group(n=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 AI-based risk and the conventional quantitative parameters including T1/2max,time ratio(TR),and rising slope(RS)were consistent when colonic perfusion was favorable as steep increasing ICG curve pattern.When the ICG graph pattern showed stepped rise,the accuracy of conventional quantitative parameters decreased,but the AI-based classification maintained accuracy consistently.The receiver operating characteristic curves for conventional parameters and AI-based classification were comparable for predicting the anastomotic complication risks.Statistical performance verifications were improved in the AI-
基金
Supported by National Research Foundation of Korea(NRF)grant funded by the Korea government(MOE),No.2020R1C1C1014421.
