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Cecilia S. Lee, James D. Brandt
Abstract
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Unresolved referenced work
Kept as external metadata until matched
A (very) brief history of artificial intelligence
2005
Methodological challenges of deep learning in optical coherence tomography for retinal diseases: a review
10.1167/tvst.9.2.11 · 2020
AAO Taskforce on AI and AAO Medical Information Technology Committee. Big data requirements for artificial intelligence
10.1097/icu.0000000000000676 · 2020
The 2016 American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight) database: characteristics and methods
10.1016/j.ophtha.2017.12.001 · 2018
The IRIS® Registry: Purpose and perspectives
10.1007/s00347-016-0265-1 · 2017
A novel biomarker identification approach for gastric cancer using gene expression and DNA methylation dataset
10.3389/fgene.2021.644378 · 2021
Smoking is associated with higher intraocular pressure regardless of glaucoma: a retrospective study of 12.5 million patients using the Intelligent Research in Sight (IRIS®) Registry
10.1016/j.ogla.2020.03.008 · 2020
Unresolved referenced work
Kept as external metadata until matched
Exploring a structural basis for delayed rod-mediated dark adaptation in age-related macular degeneration via deep learning
10.1167/tvst.9.2.62 · 2020
Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal
2021
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study
10.1016/s2589-7500(19)30108-6 · 2019
Generating retinal flow maps from structural optical coherence tomography with artificial intelligence
10.1038/s41598-019-42042-y · 2019
Forecasting future Humphrey visual fields using deep learning
10.1371/journal.pone.0214875 · 2019
PeriorbitAI: artificial intelligence automation of eyelid and periorbital measurements
2021
Putting the data before the algorithm in big data addressing personalized healthcare
10.1038/s41746-019-0157-2 · 2019
Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems
10.2337/dc20-1877 · 2021
Electronic health records in ophthalmology: source and method of documentation
10.1016/j.ajo.2019.11.030 · 2020
Unresolved referenced work
Kept as external metadata until matched
Protecting data privacy in the age of AI-enabled ophthalmology
10.1167/tvst.9.2.36 · 2020
Recommendations for standardization of images in ophthalmology
10.1016/j.ophtha.2021.03.003 · 2021
Big data and uveitis
10.1016/j.ophtha.2016.08.037 · 2016
No additional external references are available.