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Machine learning in optical coherence tomography angiography.
Exp Biol Med (Maywood). 2021 Jul 19 [Online ahead of print]EB

Abstract

Optical coherence tomography angiography (OCTA) offers a noninvasive label-free solution for imaging retinal vasculatures at the capillary level resolution. In principle, improved resolution implies a better chance to reveal subtle microvascular distortions associated with eye diseases that are asymptomatic in early stages. However, massive screening requires experienced clinicians to manually examine retinal images, which may result in human error and hinder objective screening. Recently, quantitative OCTA features have been developed to standardize and document retinal vascular changes. The feasibility of using quantitative OCTA features for machine learning classification of different retinopathies has been demonstrated. Deep learning-based applications have also been explored for automatic OCTA image analysis and disease classification. In this article, we summarize recent developments of quantitative OCTA features, machine learning image analysis, and classification.

Authors+Show Affiliations

Department of Bioengineering, 14681University of Illinois at Chicago, Chicago, IL 60607, USA.Department of Bioengineering, 14681University of Illinois at Chicago, Chicago, IL 60607, USA.Department of Bioengineering, 14681University of Illinois at Chicago, Chicago, IL 60607, USA. Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

34279136

Citation

Le, David, et al. "Machine Learning in Optical Coherence Tomography Angiography." Experimental Biology and Medicine (Maywood, N.J.), 2021, p. 15353702211026581.
Le D, Son T, Yao X. Machine learning in optical coherence tomography angiography. Exp Biol Med (Maywood). 2021.
Le, D., Son, T., & Yao, X. (2021). Machine learning in optical coherence tomography angiography. Experimental Biology and Medicine (Maywood, N.J.), 15353702211026581. https://doi.org/10.1177/15353702211026581
Le D, Son T, Yao X. Machine Learning in Optical Coherence Tomography Angiography. Exp Biol Med (Maywood). 2021 Jul 19;15353702211026581. PubMed PMID: 34279136.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Machine learning in optical coherence tomography angiography. AU - Le,David, AU - Son,Taeyoon, AU - Yao,Xincheng, Y1 - 2021/07/19/ PY - 2021/7/19/entrez PY - 2021/7/20/pubmed PY - 2021/7/20/medline KW - Retina KW - artificial intelligence KW - convolutional neural network KW - deep learning KW - machine learning KW - optical coherence tomography angiography KW - retinopathy SP - 15353702211026581 EP - 15353702211026581 JF - Experimental biology and medicine (Maywood, N.J.) JO - Exp Biol Med (Maywood) N2 - Optical coherence tomography angiography (OCTA) offers a noninvasive label-free solution for imaging retinal vasculatures at the capillary level resolution. In principle, improved resolution implies a better chance to reveal subtle microvascular distortions associated with eye diseases that are asymptomatic in early stages. However, massive screening requires experienced clinicians to manually examine retinal images, which may result in human error and hinder objective screening. Recently, quantitative OCTA features have been developed to standardize and document retinal vascular changes. The feasibility of using quantitative OCTA features for machine learning classification of different retinopathies has been demonstrated. Deep learning-based applications have also been explored for automatic OCTA image analysis and disease classification. In this article, we summarize recent developments of quantitative OCTA features, machine learning image analysis, and classification. SN - 1535-3699 UR - https://www.unboundmedicine.com/medline/citation/34279136/Machine_learning_in_optical_coherence_tomography_angiography_ L2 - https://journals.sagepub.com/doi/10.1177/15353702211026581?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -
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