AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions
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, compl...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020024 |
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| author | Mini Han Wang Lumin Xing Yi Pan Feng Gu Junbin Fang Xiangrong Yu Chi Pui Pang Kelvin Kam-Lung Chong Carol Yim-Lui Cheung Xulin Liao Xiaoxiao Fang Jie Yang Ruoyu Zhou Xiaoshu Zhou Fengling Wang Wenjian Liu |
| author_facet | Mini Han Wang Lumin Xing Yi Pan Feng Gu Junbin Fang Xiangrong Yu Chi Pui Pang Kelvin Kam-Lung Chong Carol Yim-Lui Cheung Xulin Liao Xiaoxiao Fang Jie Yang Ruoyu Zhou Xiaoshu Zhou Fengling Wang Wenjian Liu |
| author_sort | Mini Han Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b81d8db3ceab44448e3301eeef2ce200 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-b81d8db3ceab44448e3301eeef2ce2002025-02-03T09:08:16ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-06-017244548410.26599/BDMA.2023.9020024AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future DirectionsMini Han Wang0Lumin Xing1Yi Pan2Feng Gu3Junbin Fang4Xiangrong Yu5Chi Pui Pang6Kelvin Kam-Lung Chong7Carol Yim-Lui Cheung8Xulin Liao9Xiaoxiao Fang10Jie Yang11Ruoyu Zhou12Xiaoshu Zhou13Fengling Wang14Wenjian Liu15Zhuhai 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, ChinaFirst Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital, Jinan 250000, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, ChinaCollege of Staten Island, The City University of New York, NY 10314, USADepartment of Optoelectronic Engineering, Jinan University, Guangzhou 510000, ChinaZhuhai 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, ChinaDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong 999077, ChinaDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong 999077, ChinaDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong 999077, ChinaDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong 999077, ChinaZhuhai Aier Eye Hospital, Zhuhai 519000, ChinaCollege of Artificial Intelligence, Chongqing Industry and Trade Polytechnic, Chongqing 400000, ChinaFaculty of Data Science, City University of Macau, Macau 999078, ChinaCentre for Science and Technology Exchange and Cooperation between China and Portuguese-Speaking Countries, Zhuhai 519000, ChinaSchool of Artificial Intelligence, Hezhou University, Hezhou 542899, ChinaFaculty of Data Science, City University of Macau, Macau 999078, ChinaThis 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.https://www.sciopen.com/article/10.26599/BDMA.2023.9020024artificial intelligence (ai)dry eye disease (ded) detectionophthalmologymulti-source evidence |
| spellingShingle | Mini Han Wang Lumin Xing Yi Pan Feng Gu Junbin Fang Xiangrong Yu Chi Pui Pang Kelvin Kam-Lung Chong Carol Yim-Lui Cheung Xulin Liao Xiaoxiao Fang Jie Yang Ruoyu Zhou Xiaoshu Zhou Fengling Wang Wenjian Liu 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 artificial intelligence (ai) dry eye disease (ded) detection ophthalmology multi-source evidence |
| title | AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions |
| title_full | AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions |
| title_fullStr | AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions |
| title_full_unstemmed | AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions |
| title_short | AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions |
| title_sort | ai based advanced approaches and dry eye disease detection based on multi source evidence cases applications issues and future directions |
| topic | artificial intelligence (ai) dry eye disease (ded) detection ophthalmology multi-source evidence |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020024 |
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