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Open Access

AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions

Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University/The First Affiliated Hospital of Faculty of Medicine Macau University of Science and Technology), Zhuhai 519000, China; with Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong 999077, China; with Faculty of Data Science, City University of Macau, Macau 999078, China; with Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai 519000, China; and also with Perspective Technology Group, Zhuhai 519000, China
First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital, Jinan 250000, China
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
College of Staten Island, The City University of New York, NY 10314, USA
Department of Optoelectronic Engineering, Jinan University, Guangzhou 510000, China
Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University/The First Affiliated Hospital of Faculty of Medicine Macau University of Science and Technology), Zhuhai 519000, China
Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong 999077, China
Zhuhai Aier Eye Hospital, Zhuhai 519000, China
College of Artificial Intelligence, Chongqing Industry and Trade Polytechnic, Chongqing 400000, China
Faculty of Data Science, City University of Macau, Macau 999078, China
Centre for Science and Technology Exchange and Cooperation between China and Portuguese-Speaking Countries, Zhuhai 519000, China
School of Artificial Intelligence, Hezhou University, Hezhou 542899, China
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Abstract

This study explores the potential of Artificial Intelligence (AI) in early screening and prognosis of Dry Eye Disease (DED), aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners. Despite the promising opportunities, challenges such as diverse diagnostic evidence, complex etiology, and interdisciplinary knowledge integration impede the interpretability, reliability, and applicability of AI-based DED detection methods. The research conducts a comprehensive review of datasets, diagnostic evidence, and standards, as well as advanced algorithms in AI-based DED detection over the past five years. The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques: (1) those with ground truth and/or comparable standards, (2) potential AI-based methods with significant advantages, and (3) supplementary methods for AI-based DED detection. The study proposes suggested DED detection standards, the combination of multiple diagnostic evidence, and future research directions to guide further investigations. Ultimately, the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations, advanced methods, challenges, and potential future perspectives, emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.

Keywords

Artificial Intelligence (AI)Dry Eye Disease (DED) detectionophthalmologymulti-source evidence

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Big Data Mining and Analytics
Volume 7 Issue 2,
June 2024
Pages 445-484
DOI: 10.26599/BDMA.2023.9020024
Cite this article:
Wang MH, Xing L, Pan Y, et al. AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions. Big Data Mining and Analytics, 2024, 7(2): 445-484. https://doi.org/10.26599/BDMA.2023.9020024

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Received: 15 May 2023
Revised: 22 August 2023
Accepted: 30 August 2023
Published: 22 April 2024
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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