Mihara H, Nanjo S, Motoo I, Ando T, Fujinami H, Yasuda I. Artificial intelligence model on images of functional dyspepsia. Artif Intell Gastrointest Endosc 2025; 6(1): 105674 [DOI: 10.37126/aige.v6.i1.105674]
Corresponding Author of This Article
Hiroshi Mihara, MD, PhD, Associate Professor, Center for Medical Education, Sapporo Medical University, S1 W17, Chuo-ku, Sapporo 060-8556, Hokkaido, Japan. m164.tym@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Observational 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/
Hiroshi Mihara, Center for Medical Education, Sapporo Medical University, Sapporo 060-8556, Hokkaido, Japan
Hiroshi Mihara, Sohachi Nanjo, Iori Motoo, Takayuki Ando, Haruka Fujinami, Ichiro Yasuda, 3rd Department of Internal Medicine, Graduate School of Medicine, University of Toyama, Toyama 9300194, Japan
Author contributions: All authors contributed to the conception and design of the study. Mihara H collected the images, and Nanjo S, Motoo I, Ando T, and Fujinami H reviewed the images; Mihara H provided training in automated deep learning models and drafted the manuscript and all authors have confirmed the final version of the manuscript.
Institutional review board statement: The study was reviewed and approved by the Toyama University Hospital Ethics Committee (Approval No. R2021032) on 2021/05/11.
Informed consent statement: An opt-out informed consent protocol was used for the use of participant data for research purposes, and this consent procedure was also reviewed and approved by the same committee under the same approval number.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
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.
Data sharing statement: The raw images used in this study are available from the following source: Data from: Artificial Intelligence Model for Detecting Duodenal Endoscopic Changes on Images of Functional Dyspepsia Dryad Repository (https://doi.org/10.5061/dryad.pvmcvdntc).
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: Hiroshi Mihara, MD, PhD, Associate Professor, Center for Medical Education, Sapporo Medical University, S1 W17, Chuo-ku, Sapporo 060-8556, Hokkaido, Japan. m164.tym@gmail.com
Received: February 5, 2025 Revised: March 1, 2025 Accepted: March 17, 2025 Published online: March 28, 2025 Processing time: 52 Days and 19.8 Hours
Abstract
BACKGROUND
Recently, it has been suggested that the duodenum may be the pathological locus of functional dyspepsia (FD). Additionally, an image-based artificial intelligence (AI) model was shown to discriminate colonoscopy images of irritable bowel syndrome from healthy subjects with an area under the curve (AUC) 0.95.
AIM
To evaluate an AI model to distinguish duodenal images of FD patients from healthy subjects.
METHODS
Duodenal images were collected from hospital records and labeled as "functional dyspepsia" or non-FD in electronic medical records. Helicobacter pylori (HP) infection status was obtained from the Japan Endoscopy Database. Google Cloud AutoML Vision was used to classify four groups: FD/HP current infection (n = 32), FD/HP uninfected (n = 35), non-FD/HP current infection (n = 39), and non-FD/HP uninfected (n = 33). Patients with organic diseases (e.g., cancer, ulcer, postoperative abdomen, reflux) and narrow-band or dye-spread images were excluded. Sensitivity, specificity, and AUC were calculated.
RESULTS
In total, 484 images were randomly selected for FD/HP current infection, FD/HP uninfected, non-FD/current infection, and non-FD/HP uninfected. The overall AUC for the four groups was 0.47. The individual AUC values were as follows: FD/HP current infection (0.20), FD/HP uninfected (0.35), non-FD/current infection (0.46), and non-FD/HP uninfected (0.74). Next, using the same images, we constructed models to determine the presence or absence of FD in the HP-infected or uninfected patients. The model exhibited a sensitivity of 58.3%, specificity of 100%, positive predictive value of 100%, negative predictive value of 77.3%, and an AUC of 0.85 in HP uninfected patients.
CONCLUSION
We developed an image-based AI model to distinguish duodenal images of FD from healthy subjects, showing higher accuracy in HP-uninfected patients. These findings suggest AI-assisted endoscopic diagnosis of FD may be feasible.
Core Tip: This study reports a duodenal endoscopic artificial intelligence (AI) image model for detecting functional dyspepsia (FD). Endoscopic images of patients with FD typically lack labeled training data, as their alterations are imperceptible to human observers. In our previous study, we developed an AI model for irritable bowel syndrome and explored whether symptom presence or absence could serve as training labels. Our findings suggest that endoscopic duodenal images of FD patients can be distinguished with high accuracy from those of healthy individuals, stratified by HP infection status. Further investigations are warranted to assess AI's applicability in diagnosing other functional gastrointestinal disorders, as well as its potential for real-time FD image identification, investigation and treatment outcome prediction.
