Big data for imaging assessment in glaucoma
Abstract: Glaucoma is the leading cause of irreversible blindness worldwide, with many individuals unaware of their condition until advanced stages, resulting in significant visual field impairment. Despite effective treatments, over 110 million people are projected to have glaucoma by 2040. Early d...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2024-09-01
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| Series: | Taiwan Journal of Ophthalmology |
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| Online Access: | https://journals.lww.com/10.4103/tjo.TJO-D-24-00079 |
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| _version_ | 1850282548169539584 |
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| author | Douglas R. da Costa Felipe A. Medeiros |
| author_facet | Douglas R. da Costa Felipe A. Medeiros |
| author_sort | Douglas R. da Costa |
| collection | DOAJ |
| description | Abstract:
Glaucoma is the leading cause of irreversible blindness worldwide, with many individuals unaware of their condition until advanced stages, resulting in significant visual field impairment. Despite effective treatments, over 110 million people are projected to have glaucoma by 2040. Early detection and reliable monitoring are crucial to prevent vision loss. With the rapid development of computational technologies, artificial intelligence (AI) and deep learning (DL) algorithms are emerging as potential tools for screening, diagnosing, and monitoring glaucoma progression. Leveraging vast data sources, these technologies promise to enhance clinical practice and public health outcomes by enabling earlier disease detection, progression forecasting, and deeper understanding of underlying mechanisms. This review evaluates the use of Big Data and AI in glaucoma research, providing an overview of most relevant topics and discussing various models for screening, diagnosis, monitoring disease progression, correlating structural and functional changes, assessing image quality, and exploring innovative technologies such as generative AI. |
| format | Article |
| id | doaj-art-0b6039d0f18849a6997488c1fcd3c1c8 |
| institution | OA Journals |
| issn | 2211-5056 2211-5072 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Taiwan Journal of Ophthalmology |
| spelling | doaj-art-0b6039d0f18849a6997488c1fcd3c1c82025-08-20T01:47:57ZengWolters Kluwer Medknow PublicationsTaiwan Journal of Ophthalmology2211-50562211-50722024-09-0114329931810.4103/tjo.TJO-D-24-00079Big data for imaging assessment in glaucomaDouglas R. da CostaFelipe A. MedeirosAbstract: Glaucoma is the leading cause of irreversible blindness worldwide, with many individuals unaware of their condition until advanced stages, resulting in significant visual field impairment. Despite effective treatments, over 110 million people are projected to have glaucoma by 2040. Early detection and reliable monitoring are crucial to prevent vision loss. With the rapid development of computational technologies, artificial intelligence (AI) and deep learning (DL) algorithms are emerging as potential tools for screening, diagnosing, and monitoring glaucoma progression. Leveraging vast data sources, these technologies promise to enhance clinical practice and public health outcomes by enabling earlier disease detection, progression forecasting, and deeper understanding of underlying mechanisms. This review evaluates the use of Big Data and AI in glaucoma research, providing an overview of most relevant topics and discussing various models for screening, diagnosis, monitoring disease progression, correlating structural and functional changes, assessing image quality, and exploring innovative technologies such as generative AI.https://journals.lww.com/10.4103/tjo.TJO-D-24-00079artificial intelligenceartificial intelligence modelbig datadata lakedeep learninggenerative artificial intelligenceglaucomamachine learning |
| spellingShingle | Douglas R. da Costa Felipe A. Medeiros Big data for imaging assessment in glaucoma Taiwan Journal of Ophthalmology artificial intelligence artificial intelligence model big data data lake deep learning generative artificial intelligence glaucoma machine learning |
| title | Big data for imaging assessment in glaucoma |
| title_full | Big data for imaging assessment in glaucoma |
| title_fullStr | Big data for imaging assessment in glaucoma |
| title_full_unstemmed | Big data for imaging assessment in glaucoma |
| title_short | Big data for imaging assessment in glaucoma |
| title_sort | big data for imaging assessment in glaucoma |
| topic | artificial intelligence artificial intelligence model big data data lake deep learning generative artificial intelligence glaucoma machine learning |
| url | https://journals.lww.com/10.4103/tjo.TJO-D-24-00079 |
| work_keys_str_mv | AT douglasrdacosta bigdataforimagingassessmentinglaucoma AT felipeamedeiros bigdataforimagingassessmentinglaucoma |