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Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery

World Journal of Gastroenterology 2020年第44期26卷 6945-6962页

作者： Sang-Ho Park Hee-Min Park Kwang-Ryul Baek Hong-Min Ahn In Young Lee Gyung Mo Son Department of Electronic Engineering Pusan National UniversityBusan 46241South Korea Department of Surgery Pusan National University Yangsan HospitalGyeongsangnam-do 50612South Korea Department of Medicine Pusan National UniversityGyeongsangnam-do 50612South Korea Department of Surgery Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and TechnologyPusan National University Yangsan HospitalYangsan-si 50612South Korea

BACKGROUND Colonic perfusion status can be assessed easily by indocyanine green(ICG)angiography to predict ischemia related anastomotic complications during laparoscopic colorectal ***,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 ***,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 *** The ICG curve was extracted from the region of interest(ROI)set in the ICG fluorescence video of the laparoscopic colorectal ***-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)*** 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)*** predictive reliability of anastomotic complications in a trained SOM network is verified using test *** 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 *** the ICG graph pattern showed stepped rise,the accuracy of conventional quantitative parameters decreased,but the AI-based classification maintained accuracy *** receiver operating characteristic curves for conventional parameters and AI-based classification were comparable for predicting the anastomotic complication *** pe

关键词： Indocyanine green Laparoscopic Artificial intelligent Anastomotic complications Colorectal surgery microcirculation analysis

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