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
ARTICLE HIGHLIGHTS
Research background

Machine learning and artificial intelligence have the potential to revolutionize precision care in inflammatory bowel diseases. The greatest area of interest has been the application of deep learning methods in automatic tumor detection during endoscopy, yet the application of such techniques in clinical outcome prediction has been lacking.

Research motivation

Traditional approaches to clinical prediction rely on conventional statistical algorithms such as regression, which are not suitable for more complex data such as repeated biomarker measurements.

Research objectives

To determine and compare the utility of deep learning with conventional algorithms in predicting response to anti-tumor necrosis factor (anti-TNF) therapy in Crohn's disease (CD).

Research methods

A retrospective cohort of CD patients commenced on anti-TNF therapy was used to experimentally develop and cross-validate three supervised learning algorithms: (1) Statistical learning algorithm; (2) Feed-forward artificial neural network; and (3) Recurrent neural network with repeated data. Predictive utility was quantified using the area under the receiver operator characteristic curve (AUC).

Research results

Within our cohort of 146 patients, the conventional statistical learning algorithm had the weakest performance [AUC 0.659, 95% confidence interval (CI): 0.562-0.756], compared to the feed-forward artificial neural network (AUC 0.710, 95%CI: 0.622-0.799; P = 0.25 vs conventional) and the recurrent neural network using repeated biomarker measurements (AUC 0.754, 95%CI: 0.674-0.834; P = 0.036 vs conventional).

Research conclusions

Deep learning methods are feasible and have the potential for stronger predictive performance compared to conventional model building methods when applied to predicting remission after anti-TNF therapy in CD.

Research perspectives

This has been the first study to investigate the utility of deep neural networks in predicting clinical outcomes using repeated clinical data in inflammatory bowel disease. Future studies should incorporate additional data types such as genetic, imaging and endoscopic factors.