@article{Wang2024, 
author = {Mini Han Wang and Lumin Xing and Yi Pan and Feng Gu and Junbin Fang and Xiangrong Yu and Chi Pui Pang and Kelvin Kam-Lung Chong and Carol Yim-Lui Cheung and Xulin Liao and Xiaoxiao Fang and Jie Yang and Ruoyu Zhou and Xiaoshu Zhou and Fengling Wang and Wenjian Liu},
title = {AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions},
year = {2024},
journal = {Big Data Mining and Analytics},
volume = {7},
number = {2},
pages = {445-484},
keywords = {Artificial Intelligence (AI), ophthalmology, Dry Eye Disease (DED) detection, multi-source evidence},
url = {https://www.sciopen.com/article/10.26599/BDMA.2023.9020024},
doi = {10.26599/BDMA.2023.9020024},
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.}
}