Prospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Diabetes. Dec 15, 2024; 15(12): 2302-2310
Published online Dec 15, 2024. doi: 10.4239/wjd.v15.i12.2302
Screening and evaluation of diabetic retinopathy via a deep learning network model: A prospective study
Li Yao, Chan-Yuan Cao, Guo-Xiao Yu, Xu-Peng Shu, Xiao-Nan Fan, Yi-Fan Zhang
Li Yao, Chan-Yuan Cao, Guo-Xiao Yu, Xu-Peng Shu, Department of Ophthalmology, First People's Hospital of Linping District, Hangzhou 311100, Zhejiang Province, China
Xiao-Nan Fan, Yi-Fan Zhang, Department of Endocrinology, Jiangsu Provincial People's Hospital, Nanjing 210029, Jiangsu Province, China
Author contributions: Yao L wrote the manuscript; Cao CY, Yu GX, Shu XP and Fan XN collected the data; Yao L and Zhang YF guided the study; all authors reviewed, edited, and approved the final manuscript and revised it critically for important intellectual content, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work.
Institutional review board statement: This study was approved by Medical Ethics Committee of the First People's Hospital of Linping District, Hangzhou (2022042).
Clinical trial registration statement: This study is registered at https://doi.org/10.1186/ISRCTN12941197.
Informed consent statement: This study has obtained informed consent and signed treatment consent from patients and their families.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: Statistical analysis plan, informed consent form, and clinical study report will also be shared if requested. Emails could be sent to the address 13858108135@163.com to obtain the shared data.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
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: Li Yao, MM, Doctor, Department of Ophthalmology, First People's Hospital of Linping District, No. 369 Yingbin Road, Nanyuan Street, Linping District, Hangzhou 311100, Zhejiang Province, China. 13858108135@163.com
Received: August 4, 2024
Revised: September 11, 2024
Accepted: October 21, 2024
Published online: December 15, 2024
Processing time: 106 Days and 22.7 Hours
Core Tip

Core Tip: Application value of a deep learning network model based on an attention mechanism in screening diabetic retinopathy (DR). By building and optimizing a deep learning model that incorporates attention mechanisms, we analyze its accuracy and efficiency in identifying features of DR and compare it with those of traditional methods. Research has focused on key performance indicators such as the sensitivity, specificity and accuracy of the model, as well as the contribution of attention mechanisms in the feature extraction process. Ultimately, the aim is to provide a more effective screening tool to improve the accuracy of clinical diagnosis and the possibility of early intervention.