Observational Study
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 14, 2021; 27(38): 6476-6488
Published online Oct 14, 2021. doi: 10.3748/wjg.v27.i38.6476
Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study
Danny Con, Daniel R van Langenberg, Abhinav Vasudevan
Danny Con, Daniel R van Langenberg, Abhinav Vasudevan, Department of Gastroenterology and Hepatology, Eastern Health, Box Hill 3128, Victoria, Australia
Daniel R van Langenberg, Abhinav Vasudevan, Faculty of Medicine, Nursing and Health Sciences, Monash University, Box Hill 3128, Victoria, Australia
Author contributions: Con D contributed conceptualization, data collection, statistical analysis, data interpretation, manuscript drafting; van Langenberg DR contributed conceptualization, data interpretation, reviewing of manuscript critically for important intellectual content; Vasudevan A contributed conceptualization, data collection, data interpretation, reviewing of manuscript critically for important intellectual content; all authors approved the final version of the manuscript.
Institutional review board statement: This study was reviewed and approved by the Eastern Health Office of Research & Ethics (approval number: LR 61/2015).
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained retrospectively.
Conflict-of-interest statement: Con D has no relevant conflicts of interest to declare. AV has received financial support to attend educational meetings from Ferring. van Langenberg DR has served as a speaker and/or received travel support from Takeda, Ferring and Shire. He has consultancy agreements with Abbvie, Janssen and Pfizer. He received research funding grants for investigator-driven studies from Ferring, Shire and AbbVie.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
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: Danny Con, MD, Doctor, Statistician, Department of Gastroenterology and Hepatology, Eastern Health, 8 Arnold Street, Box Hill 3128, Victoria, Australia. dannycon302@gmail.com
Received: March 5, 2021
Peer-review started: March 5, 2021
First decision: April 17, 2021
Revised: April 26, 2021
Accepted: September 6, 2021
Article in press: September 6, 2021
Published online: October 14, 2021
Processing time: 221 Days and 3.2 Hours
Core Tip

Core Tip: Deep learning has vast potential, but its clinical utility in predicting outcomes in Crohn’s disease (CD) has not been explored. This study showed that deep learning algorithms (a recurrent neural network) using a more complex information structure including repeated biomarker measurements had a better predictive performance compared to a conventional statistical algorithm using only baseline data. This proof-of-concept study therefore paves the way for further research in the use of deep learning methods in clinical prediction in CD.