Version 1
: Received: 2 November 2022 / Approved: 2 November 2022 / Online: 2 November 2022 (04:21:40 CET)
How to cite:
Wang, H.; Xing, L. A Fusion-based AI Approach for Dry Eye Disease Diagnosis using Multiple Sources of Digital Ophthalmic Data: A Bibliographic Study. Preprints2022, 2022110044. https://doi.org/10.20944/preprints202211.0044.v1
Wang, H.; Xing, L. A Fusion-based AI Approach for Dry Eye Disease Diagnosis using Multiple Sources of Digital Ophthalmic Data: A Bibliographic Study. Preprints 2022, 2022110044. https://doi.org/10.20944/preprints202211.0044.v1
Wang, H.; Xing, L. A Fusion-based AI Approach for Dry Eye Disease Diagnosis using Multiple Sources of Digital Ophthalmic Data: A Bibliographic Study. Preprints2022, 2022110044. https://doi.org/10.20944/preprints202211.0044.v1
APA Style
Wang, H., & Xing, L. (2022). A Fusion-based AI Approach for Dry Eye Disease Diagnosis using Multiple Sources of Digital Ophthalmic Data: A Bibliographic Study. Preprints. https://doi.org/10.20944/preprints202211.0044.v1
Chicago/Turabian Style
Wang, H. and Lumin Xing. 2022 "A Fusion-based AI Approach for Dry Eye Disease Diagnosis using Multiple Sources of Digital Ophthalmic Data: A Bibliographic Study" Preprints. https://doi.org/10.20944/preprints202211.0044.v1
Abstract
Dry eye disease (DED) is one of the most common eye diseases. There is at least one DED patient in almost every five people. AI-based research methods increasingly become the focus of DED diagnosis research. This study utilizes a systematic review method on DED AI-based diagnosis. 2112 unduplicated records are extracted from Google Scholar, Web of Science (WOS), PubMed, China National Knowledge Infrastructure (CNKI), and Scopus databases. The most contributed countries, institutions, authors, journals, references, and disciplines are recognized. Keyword distribution and hot topics are identified. Popular databases of ophthalmic images, videos, and electronic demographic medical records are discussed. The DED diagnosis, classification, and grading criteria are identified. The major diagnosing methods are clustered, compared, and investigated. Findings show that diagnosing method research could be classified into three categories based on the relationship between AI techniques, which are (1) ground truth and/or comparable standards for AI DED diagnosis (TBUT, S Ⅰ T, TMH, and OSDI), (2) potential methods for AI-based methods have a great advantage(DED detection based on meibometry Images, CASPs, IVCM Images, OCT Images, blink videos and ultrasonic imaging), (3) and the potential direction and supplemented methods for AI-based DED detection (DED detections based on tear osmolarity, proteomic analysis, TCM and demographic information). AI-based approaches based on digital ophthalmologic images play an important role in early screening. Challenges and future perspectives are discussed at the end of this article, academically and practically.
Keywords
Dry eye disease; Artificial Intelligence; diagnosis; bibliographic study
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
