Improved Image Analysis for Measuring Gastric Ulcer Index in Animal Models and Clinical Diagnostic Data

Abstract

Gastric ulcers are one of the most common gastrointestinal diseases. In this study, as an attempt to reduce the minimal error in clinical observations during the diagnosis of gastric ulcers, the applicability of improved ImageJ analysis (IA) was investigated by comparing the results of animal experiments and clinical data. As a result, IA exhibited a significantly improved potential for determining the ulcer index (UI) of clinical data sheets compared to those rated directly by conventional clinical observation (CCO). This indicated that IA enhanced the reproducibility of the measurement of gastric UI using a Bland–Altman plot, resulting in a reduced deviation of each UI value. In addition, it was confirmed that errors in gastric UI decisions can be reduced by adjusting RGB values in diagnostic clinical data (i.e., adjusting to 100 is relatively better than adjusting to 50 or 200). Together, these results suggest that the new enhanced IA could be compatible with novel applications for measuring and evaluating gastric ulcers in clinical settings, meaning that the developed method could be used not only as an auxiliary tool for CCO, but also as a pipeline for ulcer diagnosis.