Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye
Abstract Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by the clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural damage is often characterized by neuroretinal rim (NRR) thinning of the optic nerve head, and other clinical parameters. Optical coherence tomography (OCT) is a popular and quantitative eye imaging platform for precise and reproducible measurement of such parameters in the clinic.Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and thus present challenges to clinical decision-making. To address this, large and diverse normative OCT databases with mathematically precise description of phenotypes can help with early detection and characterization of the different phenotypes that are encountered in the clinic. In this study, we generated a new large dataset of OCT generated high-resolution circular data on NRR phenotypes, along with other clinical covariates, of nearly 4,000 healthy eyes as part of a well-established clinical cohort (LVPEI-GLEAMS) of Asian Indian participants.In this study, we (1) generated high-resolution circular OCT measurements of NRR thickness in a given eye, (2) introduced CIFU, a new computational pipeline for <jats:underline>CI</jats:underline>rcular <jats:underline>FU</jats:underline>nctional data modeling and analysis that is demonstrated using the OCT dataset, and (3) addressed the disparity of representation of the Asian Indian population in normative OCT databases. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, meta-clustering to characterize the clustering output using clinical covariates, and presenting a circular visualization of the results. Upon stratification by age, we identified a healthy NRR phenotype cluster in the age group 40-49 years with predictive potential for glaucoma..
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Preprint| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
bioRxiv.org - (2022) vom: 25. Mai Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ali, Md. Hasnat [VerfasserIn] |
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| Links: |
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| doi: |
10.1101/2021.02.07.21251275 |
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| PPN (Katalog-ID): |
XBI01989127X |
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| 520 | |a Abstract Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by the clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural damage is often characterized by neuroretinal rim (NRR) thinning of the optic nerve head, and other clinical parameters. Optical coherence tomography (OCT) is a popular and quantitative eye imaging platform for precise and reproducible measurement of such parameters in the clinic.Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and thus present challenges to clinical decision-making. To address this, large and diverse normative OCT databases with mathematically precise description of phenotypes can help with early detection and characterization of the different phenotypes that are encountered in the clinic. In this study, we generated a new large dataset of OCT generated high-resolution circular data on NRR phenotypes, along with other clinical covariates, of nearly 4,000 healthy eyes as part of a well-established clinical cohort (LVPEI-GLEAMS) of Asian Indian participants.In this study, we (1) generated high-resolution circular OCT measurements of NRR thickness in a given eye, (2) introduced CIFU, a new computational pipeline for <jats:underline>CI</jats:underline>rcular <jats:underline>FU</jats:underline>nctional data modeling and analysis that is demonstrated using the OCT dataset, and (3) addressed the disparity of representation of the Asian Indian population in normative OCT databases. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, meta-clustering to characterize the clustering output using clinical covariates, and presenting a circular visualization of the results. Upon stratification by age, we identified a healthy NRR phenotype cluster in the age group 40-49 years with predictive potential for glaucoma. | ||
| 700 | 1 | |a Wainwright, Brian |e verfasserin |4 aut | |
| 700 | 1 | |a Petersen, Alexander |e verfasserin |4 aut | |
| 700 | 1 | |a Jonnadula, Ganesh B. |e verfasserin |4 aut | |
| 700 | 1 | |a Aruru, Meghana |e verfasserin |4 aut | |
| 700 | 1 | |a Rao, Harsha L. |e verfasserin |4 aut | |
| 700 | 1 | |a Srinivas, M. B. |e verfasserin |4 aut | |
| 700 | 1 | |a Jammalamadaka, S. Rao |e verfasserin |4 aut | |
| 700 | 1 | |a Senthil, Sirisha |e verfasserin |4 aut | |
| 700 | 1 | |a Pyne, Saumyadipta |e verfasserin |4 aut | |
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