학술논문
Retinal neovascularization as self-organized criticality on ultra-widefield fluorescein angiography imaging of diabetic retinopathy.
Document Type
Academic Journal
Author
Young BK; Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA.; Bommakanti N; Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA.; Yu G; Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA.; Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Palo Alto, CA, USA.; Patel TP; Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA.; Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD, USA.; Azzouz L; Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA.; Powell C; Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA.; Paulus YM; Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, MI, USA. ypaulus@med.umich.edu.; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA. ypaulus@med.umich.edu.
Source
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 8703986 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-5454 (Electronic) Linking ISSN: 0950222X NLM ISO Abbreviation: Eye (Lond) Subsets: MEDLINE
Subject
Language
English
Abstract
Background/objectives: Do the distributions of surface area of non-perfusion (NP) and neovascularization (NV) on ultra-widefield fluorescein angiography (UWF FA) in patients with diabetic retinopathy (DR) differ significantly?
Subjects/methods: Inclusion criteria were patients who had a UWF FA taken for DR at the Kellogg Eye Center from January 2009 to May 2018. Exclusion criteria included previous panretinal photocoagulation and significant media opacity (e.g., vitreous haemorrhage or significant cataract). UWF FAs were manually segmented for surface areas of NP and NV. The total areas per patient were organized in a histogram, and logarithmically binned to test against power law and exponential distributions. Then, a computational model was constructed in Python 3.7 to suggest a mechanistic explanation for the observed distributions.
Results: Analysis of areas of NV across 189 images demonstrated a superior fit by the least squares method to a power law distribution (p = 0.014) with an R2 fit of 0.9672. Areas of NP over 794 images demonstrated a superior fit with an exponential distribution instead (p = 0.011). When the far periphery was excluded, the R 2 fit for the exponential distribution was 0.9618. A computational model following the principles of self-organized criticality (SOC), akin to earthquake and forest fire models, matched these datasets.
Conclusions: These distributions inform what useful statistics may be applied to study of these imaging characteristics. Further, the difference in event distribution between NV and NP suggests that the two phenomena are mechanistically distinct. NV may follow SOC, propagating as a catastrophic event in an unpredictable manner.
(© 2023. The Author(s), under exclusive licence to The Royal College of Ophthalmologists.)
Subjects/methods: Inclusion criteria were patients who had a UWF FA taken for DR at the Kellogg Eye Center from January 2009 to May 2018. Exclusion criteria included previous panretinal photocoagulation and significant media opacity (e.g., vitreous haemorrhage or significant cataract). UWF FAs were manually segmented for surface areas of NP and NV. The total areas per patient were organized in a histogram, and logarithmically binned to test against power law and exponential distributions. Then, a computational model was constructed in Python 3.7 to suggest a mechanistic explanation for the observed distributions.
Results: Analysis of areas of NV across 189 images demonstrated a superior fit by the least squares method to a power law distribution (p = 0.014) with an R
Conclusions: These distributions inform what useful statistics may be applied to study of these imaging characteristics. Further, the difference in event distribution between NV and NP suggests that the two phenomena are mechanistically distinct. NV may follow SOC, propagating as a catastrophic event in an unpredictable manner.
(© 2023. The Author(s), under exclusive licence to The Royal College of Ophthalmologists.)