A scoping review of advancements in machine learning for glaucoma: current trends and future direction
IntroductionMachine learning technology has demonstrated significant potential in glaucoma research, particularly in early diagnosis, predicting disease progression, evaluating treatment responses, and developing personalized treatment strategies. The application of machine learning not only enhance...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1573329/full |
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| author | Jiatong Zhang Bocheng Tian Mingke Tian Xinxin Si Jiani Li Ting Fan |
| author_facet | Jiatong Zhang Bocheng Tian Mingke Tian Xinxin Si Jiani Li Ting Fan |
| author_sort | Jiatong Zhang |
| collection | DOAJ |
| description | IntroductionMachine learning technology has demonstrated significant potential in glaucoma research, particularly in early diagnosis, predicting disease progression, evaluating treatment responses, and developing personalized treatment strategies. The application of machine learning not only enhances the understanding of the pathological mechanism of glaucoma and optimizes the diagnostic process but also provides patients with accurate medical services.MethodsThis study aimed to describe the current state of research, highlight directions for further development, and identify potential trends for improvement. This review was conducted following the scoping review of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension to showcase advancements in the application of machine learning in glaucoma research and treatment.ResultsWe employed a comprehensive search strategy to retrieve literature from the Web of Science Core Collection database, ultimately including 3,581 articles in the analysis. Through data analysis, we identified current research hotspots, noted differences in researchers' attitudes and opinions, and predicted potential future development trends.DiscussionWe divided the research topics into six categories, clearly identifying “eye diseases”, “retinal fundus imaging” and “risk factors” as the key terms for the development of this field. These findings signify the promising prospects of machine learning, particularly when integrated with multimodal technologies and large language models, to enhance the diagnosis and treatment of glaucoma. |
| format | Article |
| id | doaj-art-3e59eb46543b400ba94e408dee5af809 |
| institution | OA Journals |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-3e59eb46543b400ba94e408dee5af8092025-08-20T02:24:47ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-04-011210.3389/fmed.2025.15733291573329A scoping review of advancements in machine learning for glaucoma: current trends and future directionJiatong Zhang0Bocheng Tian1Mingke Tian2Xinxin Si3Jiani Li4Ting Fan5The First Clinical Medical School, China Medical University, Shenyang, ChinaThe Second Clinical Medical School, China Medical University, Shenyang, ChinaEmory College of Arts and Sciences, Emory University, Atlanta, GA, United StatesThe Fourth Clinical Medical School, China Medical University, Shenyang, ChinaThe First Clinical Medical School, China Medical University, Shenyang, ChinaSchool of Intelligent Medicine, China Medical University, Shenyang, ChinaIntroductionMachine learning technology has demonstrated significant potential in glaucoma research, particularly in early diagnosis, predicting disease progression, evaluating treatment responses, and developing personalized treatment strategies. The application of machine learning not only enhances the understanding of the pathological mechanism of glaucoma and optimizes the diagnostic process but also provides patients with accurate medical services.MethodsThis study aimed to describe the current state of research, highlight directions for further development, and identify potential trends for improvement. This review was conducted following the scoping review of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension to showcase advancements in the application of machine learning in glaucoma research and treatment.ResultsWe employed a comprehensive search strategy to retrieve literature from the Web of Science Core Collection database, ultimately including 3,581 articles in the analysis. Through data analysis, we identified current research hotspots, noted differences in researchers' attitudes and opinions, and predicted potential future development trends.DiscussionWe divided the research topics into six categories, clearly identifying “eye diseases”, “retinal fundus imaging” and “risk factors” as the key terms for the development of this field. These findings signify the promising prospects of machine learning, particularly when integrated with multimodal technologies and large language models, to enhance the diagnosis and treatment of glaucoma.https://www.frontiersin.org/articles/10.3389/fmed.2025.1573329/fullmachine learningglaucoma diagnosisdeep learningmultimodal imagingophthalmic research |
| spellingShingle | Jiatong Zhang Bocheng Tian Mingke Tian Xinxin Si Jiani Li Ting Fan A scoping review of advancements in machine learning for glaucoma: current trends and future direction Frontiers in Medicine machine learning glaucoma diagnosis deep learning multimodal imaging ophthalmic research |
| title | A scoping review of advancements in machine learning for glaucoma: current trends and future direction |
| title_full | A scoping review of advancements in machine learning for glaucoma: current trends and future direction |
| title_fullStr | A scoping review of advancements in machine learning for glaucoma: current trends and future direction |
| title_full_unstemmed | A scoping review of advancements in machine learning for glaucoma: current trends and future direction |
| title_short | A scoping review of advancements in machine learning for glaucoma: current trends and future direction |
| title_sort | scoping review of advancements in machine learning for glaucoma current trends and future direction |
| topic | machine learning glaucoma diagnosis deep learning multimodal imaging ophthalmic research |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1573329/full |
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