Copyright
©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Aug 14, 2020; 26(30): 4453-4464
Published online Aug 14, 2020. doi: 10.3748/wjg.v26.i30.4453
Published online Aug 14, 2020. doi: 10.3748/wjg.v26.i30.4453
Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence
In Woong Han, Youngju Ryu, Sang Hyun Shin, Jin Seok Heo, Dong Wook Choi, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea
Kyeongwon Cho, Myung Jin Chung, Baek Hwan Cho, Medical Artificial Intelligence Research Center, Department of Medical Device Management and Research, SAIHST, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea
Myung Jin Chung, Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea
Oh Chul Kwon, Artificial Intelligence Research Center, Medical DataBase Incorporated, Seoul 06048, South Korea
Author contributions: Han IW and Cho K contributed equally to this work; Han IW and Cho BH designed the research the paper; Han IW, Cho K, and Kwon OC performed the research and wrote the paper; Ryu Y, Shin SH, Heo JS, and Choi DW contributed to the analysis and provided clinical advice; Chung MJ and Cho BH supervised the report.
Supported by the National Research Foundation of Korea grant funded by the Korea government (Ministry of Science and ICT) , No. NRF-2019R1F1A1042156 ; and the Bio & Medical Technology Development Program , No. NRF-2017M3A9E1064784 .
Institutional review board statement: This study was reviewed and approved by Institutional review board of Samsung Medical Center (number: SMC 2017-01-017).
Informed consent statement: Patients were not required to give informed consent to this retrospective study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: There are no financial or any potential personal conflicts of interest to declare for any of the authors.
Data sharing statement: No additional data are available.
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: Baek Hwan Cho, PhD, Assistant Professor, Medical AI Research Center, Department of Medical Device Management and Research, SAIHST, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, South Korea. baekhwan.cho@samsung.com
Received: April 22, 2020
Peer-review started: April 22, 2020
First decision: April 29, 2020
Revised: July 13, 2020
Accepted: July 30, 2020
Article in press: July 30, 2020
Published online: August 14, 2020
Peer-review started: April 22, 2020
First decision: April 29, 2020
Revised: July 13, 2020
Accepted: July 30, 2020
Article in press: July 30, 2020
Published online: August 14, 2020
Core Tip
Core tip: Postoperative pancreatic fistula (POPF) is a life-threatening complication following pancreatoduodenectomy. This is a retrospective study to develop a risk prediction platform for POPF using an Artificial intelligence (AI) model. Compared with established POPF risk prediction methods, this machine learning algorithms better predict the POPF risk correctly (AUC 0.74). This AI-driven platform can identify patients who need especially intense therapy and aid in the establishment of an effective treatment strategy.