Copyright
©The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved.
Non-invasive prediction of non-alcoholic steatohepatitis in Japanese patients with morbid obesity by artificial intelligence using rule extraction technology
Daisuke Uehara, Yoichi Hayashi, Yosuke Seki, Satoru Kakizaki, Norio Horiguchi, Hiroki Tojima, Yuichi Yamazaki, Ken Sato, Kazuki Yasuda, Masanobu Yamada, Toshio Uraoka, Kazunori Kasama
Daisuke Uehara, Masanobu Yamada, Department of Medicine and Molecular Science, Gunma University Graduate School of Medicine, Maebashi, Gunma 371-8511, Japan
Daisuke Uehara, Satoru Kakizaki, Norio Horiguchi, Hiroki Tojima, Yuichi Yamazaki, Ken Sato, Toshio Uraoka, Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine, Maebashi, Gunma 371-8511, Japan
Yoichi Hayashi, Department of Computer Science, Meiji University, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan
Yosuke Seki, Kazunori Kasama, Weight Loss and Metabolic Surgery Center, Yotsuya Medical Cube, Tokyo 102-0084, Japan
Kazuki Yasuda, Department of Metabolic Disorder, Diabetes Research Center, Research Institute, National Center for Global Health and Medicine, Tokyo 162-8655, Japan.
Masanobu Yamada, Department of Internal Medicine, Division of Endocrinology and Metabolism, Gunma University Graduate School of Medicine, Maebashi, Gunma 371-8511, Japan
Author contributions: Uehara D, Hayashi Y, Seki Y and Kakizaki S designed research; Uehara D, Horiguchi N, Yamazaki Y, Sato K and Yasuda D contributed to data acquisition; Hayashi Y analyzed data using artificial intelligence; Uehara D and Tojima H performed statistical analyses; and Yamada M, Uraoka T and Kasama K supervised the study.
Institutional review board statement: This study was approved by institutional review boards of Yotsuya Medical Cube and Gunma University.
Informed consent statement: For this study using artificial intelligence using rule extraction technology, opt-out was obtained.
Conflict-of-interest statement: There are no conflicts of interest.
Data sharing statement: No additional data is available
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: Satoru Kakizaki, MD, PhD, Associate Professor, Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine, Showa-machi, 3-39-15, Maebashi 371-8511, Gunma, Japan. kakizaki@gunma-u.ac.jp
Telephone: +81-27-2208127 Fax: +81-27-2208136
Received: June 26, 2018
Peer-review started: July 2, 2018
First decision: August 1, 2018
Revised: August 20, 2018
Accepted: October 17, 2018
Article in press: October 17, 2018
Published online: December 27, 2018
ARTICLE HIGHLIGHTS
Research background
Pathological diagnosis is the gold standard for the diagnosis of non-alcoholic steatohepatitis (NASH). However, it is difficult to perform a percutaneous liver biopsy in routine medical care, especially in morbidly obese patients. Predictive calculation formulas are usually used to diagnose fibrosis and NASH.
Research motivation
Recently, there has been remarkable progress in artificial intelligence. We therefore attempted to construct a non-invasive prediction algorithm in order to predict NASH using artificial intelligence with rule extraction technology.
Research objectives
Morbidly obese Japanese patients who required bariatric surgery underwent a liver biopsy during the operation. Standard clinical, anthropometric, biochemical measurements were used as parameters for making prediction model.
Research methods
One hundred and two patients, including 79 NASH and 23 non-NASH patients were analyzed in order to create the prediction model, another cohort with 77 patients including 65 NASH and 12 non-NASH patients were analyzed to validate the algorithm. We used Continuous recursive-rule extraction with J48graft for rule extraction to predict NASH.
Research results
Alanine aminotransferase, C-reactive protein, homeostasis model assessment insulin resistance, albumin were extracted as predictors of NASH. When we adopted the extracted rules for the validation cohort, the predictive accuracy was 79.2%. The positive predictive value, negative predictive value, sensitivity and specificity were 88.9%, 35.7%, 86.2% and 41.7%, respectively.
Research conclusions
We successfully generated a useful model for predicting NASH in Japanese morbidly obese patients based on their biochemical profile using a rule extraction algorithm.
Research perspectives
Although further studies with larger numbers of patients are needed to confirm the results, this algorithm may be useful for non-invasively predicting NASH in morbidly obese Japanese patients in the clinical setting.
