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Articles in Press
7/30/2020 5:36:58 AM | Browse: 162 | Download: 357
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
In Woong Han, Kyeongwon Cho, Youngju Ryu, Sang Hyun Shin, Jin Seok Heo, Dong Wook Choi, Myung Jin Chung, Oh Chul Kwon and Baek Hwan Cho |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| the National Research Foundation of Korea grant funded by the Korea government (Ministry of Science and ICT) |
NRF-2019R1F1A1042156 |
| the Bio & Medical Technology Development Program |
NRF-2017M3A9E1064784 |
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| Corresponding Author |
Baek Hwan Cho, PhD, Director, 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 |
| Key Words |
Postoperative pancreatic fistula; Pancreatoduodenectomy; Neural networks; Recursive feature elimination; ; |
| 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. |
| Citation |
Han IW, Cho K, Ryu Y, Shin SH, Heo JS, Choi DW, Chung MJ, Kwon OC, Cho BH. Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence. World J Gastroenterol 2020; 26(30): 4453-4464 |
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Received |
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2020-04-22 05:08 |
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Peer-Review Started |
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2020-04-22 05:08 |
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To Make the First Decision |
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Return for Revision |
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2020-04-29 23:35 |
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Revised |
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2020-07-13 02:54 |
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Second Decision |
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2020-07-28 12:00 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Company Editor-in-Chief |
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2020-07-30 05:36 |
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Articles in Press |
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2020-07-30 05:36 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2020-08-14 00:53 |
| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| 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/ |
| Copyright |
© The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
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