Published online Nov 28, 2020. doi: 10.3748/wjg.v26.i44.6945
Peer-review started: July 6, 2020
First decision: October 18, 2020
Revised: October 28, 2020
Accepted: November 9, 2020
Article in press: November 9, 2020
Published online: November 28, 2020
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).
To evaluate the feasibility of AIRAM to predict the risk of anastomotic complication in the patient with laparoscopic colorectal cancer surgery.
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.
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-based analysis. AI analysis was evaluated as the most accurate parameter to predict the risk of anastomotic complications. The F1 score of the AI-based method increased by 31% for T1/2max, 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.
In conclusion, AI-based real-time microcirculation analysis had more accurate and consistent performance than the conventional parameter-based method.
Core Tip: This study provides an artificial intelligence-based analysis method in indocyanine green (ICG) angiography to predict anastomotic complications after laparoscopic colonic surgery. Using a self-organizing map network, ICG curves were classified and machine learned into 25 patterns, and real-time microcirculation analysis can be performed during surgery by the blood flow of each pattern calculated in advance. Such real-time analysis of perfusion during surgery may reduce the probability of post-laparoscopic colorectal anastomotic complications. This study additionally requires clinical trials.