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
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, 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: http://creativecommons.org/licenses/by-nc/4.0/
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
Processing time: 143 Days and 22.2 Hours
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
Processing time: 143 Days and 22.2 Hours
Core Tip
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.