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Journal
/
Biology
/
Modeling and Analyzing Retinal Degeneration
/
LipidUNet-Machine Learning Based Method of Characterization and Quantification of Lipid Deposits Using iPSC-Derived Retinal Pigment Epithelium
JoVE Journal
Biology
This content is Free Access.
JoVE Journal
Biology
LipidUNet-Machine Learning-Based Method of Characterization and Quantification of Lipid Deposits Using iPSC-Derived Retinal Pigment Epithelium
Please note that all translations are automatically generated.
Click here for the English version.
LipidUNet-Machine Learning Based Method of Characterization and Quantification of Lipid Deposits Using iPSC-Derived Retinal Pigment Epithelium
Modeling and Analyzing Retinal Degeneration
DOI:
10.3791/65503-v
•
00:49 min
•
July 28, 2023
•
Zander Esh*
1
,
Sharanya Suresh*
1
,
Davide Ortolan
,
Mitra Farnoodian
,
Devika Bose
,
Jiwon Ryu
,
Andrei Volkov
,
Kapil Bharti
,
Ruchi Sharma
1
Ocular and Stem Cell Translational Research Section, National Eye Institute
,
National Institutes of Health (NIH)
Tags
Keywords: Lipid Deposits
Retinal Pigment Epithelium
Age-related Macular Degeneration
Induced Pluripotent Stem Cell
Machine Learning
LipidUNet
Lipid Droplet Analysis
Retinal Degeneration
Stargardt Disease
Late-onset Retinal Degeneration
Complement Activation
Phagocytosis
Drug Discovery
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