Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 119, Issue 19, Pages -Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2117553119
Keywords
cell morphometry; retinal degeneration; artificial intelligence; data science; AMD
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Funding
- NEI IRP funds [ZIA EY000533-04]
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This research utilized artificial intelligence-based software to develop a complete morphometric map of RPE in the human eye, identifying five distinct RPE subpopulations and revealing their differential vulnerability to various retinal diseases.
Regional phenotypic and functional differences in the retinal pigment epithelium (RPE) monolayer have been suggested to account for regional susceptibility in ocular diseases such as age-related macular degeneration (AMD), late-onset retinal degeneration (L-ORD), and choroideremia (CHM). However, a comprehensive description of human topographical RPE diversity is not yet available, thus limiting the understanding of regional RPE diversity and degenerative disease sensitivity in the eye. To develop a complete morphometric RPE map of the human eye, artificial intelligence-based software was trained to recognize, segment, and analyze RPE borders. Five statistically different, concentric RPE subpopulations (P1 to P5) were identified using cell area as a parameter, including a subpopulation (P4) with cell area comparable to that of macular cells in the far periphery of the eye. This work provides a complete reference map of human RPE subpopulations and their location in the eye. In addition, the analysis of cadaver non-AMD and AMD eyes and ultra-widefield fundus images of patients revealed differential vulnerability of the five RPE subpopulations to different retinal diseases.
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