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Article Abstract
Purpose: To evaluate the severity scales of diabetic macular ischemia (DMI) by analyzing the quantity and distribution of capillary nonperfusion using OCT angiography (OCTA) images.
Design: A single-center, prospective case series.
Participants: Three hundred one eyes from 301 patients with diabetic retinopathy.
Methods: We acquired 3 × 3-mm swept-source OCTA images and created en face images within a central 2.5-mm circle. The circle was divided into 15 × 15-pixel squares and nonperfusion squares (NPSs) were defined as those without retinal vessels. Eyes with high-dimensional spatial data were arranged on a 2-dimensional space using the uniform manifold approximation and projection (UMAP) algorithm and classified by clustering into 5 groups: , , , , and .
Main Outcome Measures: Development of a severity scale for DMI.
Results: Eyes arranged on a 2-dimensional UMAP space were divided into 5 clusters, based on the similarity of nonperfusion area distribution. Nonperfusion square counts in the deep layer increased in eyes of the , , , and groups in a stepwise manner. In contrast, there were no significant changes in superficial NPS counts between eyes of the and groups. In the intermediate stage, eyes of the group exhibited higher NPS counts in the central sector of the superficial layer compared with those of the group. The foveal avascular zone extended into the temporal subfield of the deep layer in eyes of the group. Eyes of the group had significantly poorer visual acuity that was more frequently accompanied with proliferative diabetic retinopathy.
Conclusions: The application of dimensionality reduction and clustering has facilitated the development of a novel severity scale for DMI based on the distribution of capillary nonperfusion in OCTA images.
Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11462257 | PMC |
| http://dx.doi.org/10.1016/j.xops.2024.100603 | DOI Listing |
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