Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications
Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data ava...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Ophthalmology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fopht.2022.1057896/full |
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| author | Da Ma Da Ma Da Ma Louis R. Pasquale Michaël J. A. Girard Michaël J. A. Girard Michaël J. A. Girard Christopher K. S. Leung Yali Jia Marinko V. Sarunic Marinko V. Sarunic Rebecca M. Sappington Rebecca M. Sappington Kevin C. Chan Kevin C. Chan |
| author_facet | Da Ma Da Ma Da Ma Louis R. Pasquale Michaël J. A. Girard Michaël J. A. Girard Michaël J. A. Girard Christopher K. S. Leung Yali Jia Marinko V. Sarunic Marinko V. Sarunic Rebecca M. Sappington Rebecca M. Sappington Kevin C. Chan Kevin C. Chan |
| author_sort | Da Ma |
| collection | DOAJ |
| description | Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data. |
| first_indexed | 2024-04-11T00:59:13Z |
| format | Article |
| id | doaj.art-d1df3a4b62d44d70a11f6f2797afe1ef |
| institution | Directory Open Access Journal |
| issn | 2674-0826 |
| language | English |
| last_indexed | 2025-03-21T01:21:15Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Ophthalmology |
| spelling | doaj.art-d1df3a4b62d44d70a11f6f2797afe1ef2024-08-03T02:20:11ZengFrontiers Media S.A.Frontiers in Ophthalmology2674-08262023-01-01210.3389/fopht.2022.10578961057896Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applicationsDa Ma0Da Ma1Da Ma2Louis R. Pasquale3Michaël J. A. Girard4Michaël J. A. Girard5Michaël J. A. Girard6Christopher K. S. Leung7Yali Jia8Marinko V. Sarunic9Marinko V. Sarunic10Rebecca M. Sappington11Rebecca M. Sappington12Kevin C. Chan13Kevin C. Chan14School of Medicine, Wake Forest University, Winston-Salem, NC, United StatesAtrium Health Wake Forest Baptist Medical Center, Winston-Salem, NC, United StatesSchool of Engineering Science, Simon Fraser University, Burnaby, BC, CanadaDepartment of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, United StatesOphthalmic Engineering & Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, SingaporeDuke-NUS Medical School, Singapore, SingaporeInstitute for Molecular and Clinical Ophthalmology, Basel, SwitzerlandDepartment of Ophthalmology, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaCasey Eye Institute, Oregon Health & Science University, Portland, OR, United States0Institute of Ophthalmology, University College London, London, United KingdomSchool of Engineering Science, Simon Fraser University, Burnaby, BC, CanadaSchool of Medicine, Wake Forest University, Winston-Salem, NC, United StatesAtrium Health Wake Forest Baptist Medical Center, Winston-Salem, NC, United States1Departments of Ophthalmology and Radiology, Neuroscience Institute, NYU Grossman School of Medicine, NYU Langone Health, New York University, New York, NY, United States2Department of Biomedical Engineering, Tandon School of Engineering, New York University, New York, NY, United StatesArtificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.https://www.frontiersin.org/articles/10.3389/fopht.2022.1057896/fulldeep learningartificial intelligencereverse translationtransfer learningglaucomaoptical coherence tomography |
| spellingShingle | Da Ma Da Ma Da Ma Louis R. Pasquale Michaël J. A. Girard Michaël J. A. Girard Michaël J. A. Girard Christopher K. S. Leung Yali Jia Marinko V. Sarunic Marinko V. Sarunic Rebecca M. Sappington Rebecca M. Sappington Kevin C. Chan Kevin C. Chan Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications Frontiers in Ophthalmology deep learning artificial intelligence reverse translation transfer learning glaucoma optical coherence tomography |
| title | Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications |
| title_full | Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications |
| title_fullStr | Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications |
| title_full_unstemmed | Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications |
| title_short | Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications |
| title_sort | reverse translation of artificial intelligence in glaucoma connecting basic science with clinical applications |
| topic | deep learning artificial intelligence reverse translation transfer learning glaucoma optical coherence tomography |
| url | https://www.frontiersin.org/articles/10.3389/fopht.2022.1057896/full |
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