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 clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural dam...

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Autores principales:	Md. Hasnat Ali, Brian Wainwright, Alexander Petersen, Ganesh B. Jonnadula, Meghana Desai, Harsha L. Rao, M. B. Srinivas, S. Rao Jammalamadaka, Sirisha Senthil, Saumyadipta Pyne
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b106e7da3b214dc99936adae4b60d4a6
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spelling	oai:doaj.org-article:b106e7da3b214dc99936adae4b60d4a62021-12-05T12:15:56ZCircular functional analysis of OCT data for precise identification of structural phenotypes in the eye10.1038/s41598-021-02025-42045-2322https://doaj.org/article/b106e7da3b214dc99936adae4b60d4a62021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02025-4https://doaj.org/toc/2045-2322Abstract Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by 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. Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and present challenges to clinical decision-making. We generated a dataset of Optical Coherence Tomography (OCT) based high-resolution circular measurements on NRR phenotypes, along with other clinical covariates, of 3973 healthy eyes as part of an established clinical cohort of Asian Indian participants. We introduced CIFU, a new computational pipeline for CIrcular FUnctional data modeling and analysis. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, followed by meta-clustering to characterize the clusters using clinical covariates, and presented 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. Our dataset also addresses the disparity of representation of this particular population in normative OCT databases.Md. Hasnat AliBrian WainwrightAlexander PetersenGanesh B. JonnadulaMeghana DesaiHarsha L. RaoM. B. SrinivasS. Rao JammalamadakaSirisha SenthilSaumyadipta PyneNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Md. Hasnat Ali Brian Wainwright Alexander Petersen Ganesh B. Jonnadula Meghana Desai Harsha L. Rao M. B. Srinivas S. Rao Jammalamadaka Sirisha Senthil Saumyadipta Pyne Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye
description	Abstract Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by 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. Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and present challenges to clinical decision-making. We generated a dataset of Optical Coherence Tomography (OCT) based high-resolution circular measurements on NRR phenotypes, along with other clinical covariates, of 3973 healthy eyes as part of an established clinical cohort of Asian Indian participants. We introduced CIFU, a new computational pipeline for CIrcular FUnctional data modeling and analysis. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, followed by meta-clustering to characterize the clusters using clinical covariates, and presented 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. Our dataset also addresses the disparity of representation of this particular population in normative OCT databases.
format	article
author	Md. Hasnat Ali Brian Wainwright Alexander Petersen Ganesh B. Jonnadula Meghana Desai Harsha L. Rao M. B. Srinivas S. Rao Jammalamadaka Sirisha Senthil Saumyadipta Pyne
author_facet	Md. Hasnat Ali Brian Wainwright Alexander Petersen Ganesh B. Jonnadula Meghana Desai Harsha L. Rao M. B. Srinivas S. Rao Jammalamadaka Sirisha Senthil Saumyadipta Pyne
author_sort	Md. Hasnat Ali
title	Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye
title_short	Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye
title_full	Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye
title_fullStr	Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye
title_full_unstemmed	Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye
title_sort	circular functional analysis of oct data for precise identification of structural phenotypes in the eye
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b106e7da3b214dc99936adae4b60d4a6
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_version_	1718372096050987008

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

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