Basic Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Nov 28, 2020; 26(44): 6945-6962
Published online Nov 28, 2020. doi: 10.3748/wjg.v26.i44.6945
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
Sang-Ho Park, Hee-Min Park, Kwang-Ryul Baek, Department of Electronic Engineering, Pusan National University, Busan 46241, South Korea
Hong-Min Ahn, Department of Surgery, Pusan National University Yangsan Hospital, Gyeongsangnam-do 50612, South Korea
In Young Lee, Department of Medicine, Pusan National University, Gyeongsangnam-do 50612, South Korea
Gyung Mo Son, Department of Surgery, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan-si 50612, South Korea
Author contributions: Son GM conceptualized the study; Son GM, Park SH and Ahn HM analyzed the formula; Son GM, Park SH and Lee IY investigated data; Baek KR, Park HM, Park SH and Son GM designed the methodology; Son GM and Baek KR administrated project; Park SH, Park HM and Son GM wrote the paper.
Supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MOE), No. 2020R1C1C1014421.
Institutional review board statement: The study was reviewed and approved by the Yangsan Pusan National University Hospital Institutional Review Board (Approval No. 05-2018-152).
Conflict-of-interest statement: The authors have nothing to disclose.
Data sharing statement: No additional data are available.
ARRIVE guidelines statement: The authors have read the ARRIVE Guidelines, and the manuscript was prepared and revised according to the ARRIVE Guidelines.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Gyung Mo Son, MD, PhD, FACS, Associate Professor, Surgeon, Department of Surgery, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 20, Geumo-ro, Mulgeum-eup, Yangsan-si 50612, South Korea.
Received: July 6, 2020
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
Research background

One of the causes of complications after surgery for colon cancer is poor perfusion at the anastomosis site. Microcirculation analysis is required to reduce anastomosis complications.

Research motivation

Conventional methods evaluated the risk of anastomotic complications by analyzing indocyanine green (ICG) curves and calculating quantitative parameters. However, there is a disadvantage in that the evaluation performance changes according to the pattern of the ICG curve.

Research objectives

To evaluate the feasibility of artificial intelligence (AI) based real-time analysis microperfusion (AIRAM) to predict the risk of anastomotic complication in the patient with laparoscopic colorectal cancer surgery.

Research 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. 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.

Research 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-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.

Research conclusions

AI-based real-time microcirculation analysis had more accurate and consistent performance than the conventional parameter-based method. In addition, real-time analysis and intuitive color map of perfusion status allow AI-based analysis system to be available during the laparoscopic or robotic colorectal surgery.

Research perspectives

Real-time analysis of perfusion during surgery may reduce the probability of post-laparoscopic colorectal anastomotic complications. This study additionally requires clinical trials.