Kusano Y, Funada K, Yamaguchi M, Sugawara M, Tamano M. Dietary counseling based on artificial intelligence for patients with nonalcoholic fatty liver disease. Artif Intell Gastroenterol 2022; 3(4): 105-116 [DOI: 10.35712/aig.v3.i4.105]
Corresponding Author of This Article
Masaya Tamano, PhD, Professor, Department of Gastroenterology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minami-Koshigaya, Koshigaya 343-8555, Saitama, Japan. mstamano@dokkyomed.ac.jp
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Prospective Study
Open-Access Policy of This Article
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/
Yumi Kusano, Kei Funada, Mayumi Yamaguchi, Masaya Tamano, Department of Gastroenterology, Dokkyo Medical University Saitama Medical Center, Koshigaya 343-8555, Saitama, Japan
Miwa Sugawara, Nutrition Unit, Dokkyo Medical University Saitama Medical Center, Koshigaya 343-8555, Saitama, Japan
Author contributions: Kusano Y reviewed the literature and contributed to manuscript drafting; Funada K analyzed and interpreted the imaging findings; Yamaguchi M drafted the tables and figures; Sugawara M conducted dietary counseling for patients; Tamano M revised the manuscript for important intellectual content; all authors issued final approval for the version to be submitted.
Institutional review board statement: Approval was obtained from the Biomedical Ethics Committee of the authors’ affiliated hospital (No. 2014).
Clinical trial registration statement: The clinical trial is registered with clinical research support office of the authors' affiliated hospital. Details can be found at https://dept.dokkyomed.ac.jp/dep-k/gast/.
Informed consent statement: Written informed consent was obtained from all patients for publication of this report and any accompanying images.
Conflict-of-interest statement: None of the authors have any commercial or financial involvements in connection with this study that represent or appear to represent any conflicts of interest.
Data sharing statement: Participants gave informed consent for data sharing.
CONSORT 2010 statement: The authors have read the CONSORT Statement—checklist of items, and the manuscript was prepared and revised according to the CONSORT 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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Masaya Tamano, PhD, Professor, Department of Gastroenterology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minami-Koshigaya, Koshigaya 343-8555, Saitama, Japan. mstamano@dokkyomed.ac.jp
Received: June 22, 2022 Peer-review started: June 22, 2022 First decision: July 11, 2022 Revised: July 13, 2022 Accepted: October 26, 2022 Article in press: October 26, 2022 Published online: October 28, 2022 Processing time: 128 Days and 0.2 Hours
ARTICLE HIGHLIGHTS
Research background
Approximately 27000 people a year die from liver cancer in Japan. Liver cancer from non-viral liver disease increases while cancerogenesis from viral liver decreases. In the non-viral liver disease, nonalcoholic fatty liver disease (NAFLD) increases in particular. Therefore, carcinogenesis restraint from NAFLD is urgent business to reduce liver cancer death. Diet therapy is the first choice for the treatment of NAFLD and nutrition education for this purpose becomes extremely important.
Research motivation
The authors paid attention to the nutrition education using the artificial intelligence and led to the idea of this study using the application software called the "Calomeal". The authors have the patients understand the importance of the diet by performing the nutrition education using the artificial intelligence for the NAFLD patients and want to help inhibit the cancerogenesis from NAFLD. A study on optimization of the nutrition education using the artificial intelligence (AI) for NAFLD is the attempt that leads the world and thinks with pioneer positioning of the future health promotion medical care.
Research objectives
Patients clinically diagnosed with NAFLD between August 2020 and March 2022 were included as subjects. "Calomeal" as a software application developed by Life Log Technology, Inc. (Tokyo, Japan) was used for the nutrition education. Blood biochemistry tests were performed before (baseline) and 6 mo after (6M follow-up) dietary counseling. After the dietary counseling, the patients were asked to complete a questionnaire survey.
Research methods
There were significant decreases in liver enzyme and triglyceride levels at the 6M follow-up compared to baseline. The food analysis capability of the AI used by Calomeal in this study was 75.1%. Patient satisfaction with the AI-based dietary counselling was high.
Research results
The authors have the patients understand the importance of the diet because the NAFLD patients receive a nutrition education using the artificial intelligence, and the purpose of this study is to carry a help of the cancerogenesis restraint.
Research conclusions
When an AI-based nutrition management software application automatically analyzed images of meals photographed by NAFLD patients, liver function was improved significantly. On the other hand, due to the limitations of the food analysis capabilities of AI, improvements in the analytical capabilities of AI are much anticipated.
Research perspectives
The direction of future research is nutrition education using more advanced artificial intelligence to inhibit the carcinogenesis from NAFLD.
