Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Endosc. May 16, 2022; 14(5): 311-319
Published online May 16, 2022. doi: 10.4253/wjge.v14.i5.311
Recognition of esophagitis in endoscopic images using transfer learning
Elena Caires Silveira, Caio Fellipe Santos Corrêa, Leonardo Madureira Silva, Bruna Almeida Santos, Soraya Mattos Pretti, Fabrício Freire de Melo
Elena Caires Silveira, Caio Fellipe Santos Corrêa, Leonardo Madureira Silva, Bruna Almeida Santos, Soraya Mattos Pretti, Fabrício Freire de Melo, Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista 45029-094, Bahia, Brazil
Author contributions: Caires Silveira E proceeded the data collection/entry, performed data analysis and data interpretation, developed the proposed predictive model and participated in preparation and review of manuscript; Santos Corrêa CF and Madureira Silva L participated in preparation of manuscript and wrote the literature analysis/search; Mattos Pretti S and Almeida Santos B participated in review of manuscript; Freire de Melo F designed the research and participated in review of manuscript.
Institutional review board statement: For this study, there was no need for an appraisal by an ethics committee, since only publicly available anonymized data were used.
Informed consent statement: The present manuscript used anonymous images to produce its analyzes and results, in a method that obeys the norms of medical bioethics. Thus, there was no direct or even indirect contact between researchers and patients, with no necessity for "Signed Informed Consent Form" to carry out our study.
Conflict-of-interest statement: The authors have no financial relationships to disclose.
Data sharing statement: No additional data are available.
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: Fabrício Freire de Melo, PhD, Professor, Multidisciplinary Institute of Health, Federal University of Bahia, Hormindo Barros Street, 58, Candeias, Vitória da Conquista 45029-094, Bahia, Brazil. freiremelo@yahoo.com.br
Received: May 13, 2021
Peer-review started: May 13, 2021
First decision: July 4, 2021
Revised: July 15, 2021
Accepted: April 27, 2022
Article in press: April 27, 2022
Published online: May 16, 2022
Processing time: 368 Days and 9.4 Hours
Core Tip

Core Tip: Considering the clinical relevance of esophagitis, we proposed a deep learning model for its diagnosis from endoscopic images of the Z-line, via binary classification of the images according to the presence or absence of esophageal inflammation signs. The excellent accuracy and area under the receiver operating characteristic curve achieved demonstrate the potential of the adopted strategy, consisting of the conjunction of densely connected neural networks and transfer learning. With this, we contribute to the improvement and methodological advancement in the development of automated diagnostic tools for the disease, which reveal great potential in optimizing the management of these patients.