Discrimination of Diabetic Retinopathy From Optical Coherence Tomography Angiography Images Using Machine Learning Methods

The goal was to discriminate between diabetic retinopathy (DR) and healthy controls (HC) by evaluating Optical coherence tomography angiography (OCTA) images from <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> mm scans with the...

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Autores principales:	Zhiping Liu, Chen Wang, Xiaodong Cai, Hong Jiang, Jianhua Wang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Diabetic retinopathy machine learning logistic regression logistic regression regularized with the elastic net penalty support vector machine Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/f84a093729d340cca6d0ff2bfd688b14
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spelling	oai:doaj.org-article:f84a093729d340cca6d0ff2bfd688b142021-11-24T00:00:20ZDiscrimination of Diabetic Retinopathy From Optical Coherence Tomography Angiography Images Using Machine Learning Methods2169-353610.1109/ACCESS.2021.3056430https://doaj.org/article/f84a093729d340cca6d0ff2bfd688b142021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9344690/https://doaj.org/toc/2169-3536The goal was to discriminate between diabetic retinopathy (DR) and healthy controls (HC) by evaluating Optical coherence tomography angiography (OCTA) images from <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> mm scans with the assistance of different machine learning models. The OCTA angiography dataset of superficial vascular plexus (SVP), deep vascular plexus (DVP), and retinal vascular network (RVN) were acquired from 19 DR (38 eyes) patients and 25 HC (44 eyes). A discrete wavelet transform was applied to extract texture features from each image. Four machine learning models, including logistic regression (LR), logistic regression regularized with the elastic net penalty (LR-EN), support vector machine (SVM), and the gradient boosting tree named XGBoost, were used to classify wavelet features between groups. The area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and diagnostic accuracy of the classifiers were obtained. The OCTA image dataset included 114 and 132 images from DR and HC subjects, respectively. LR-EN and LR using all three images, SVP, DVP, and RVN, provided the highest sensitivity of 0.84 and specificity of 0.80, the best diagnostic accuracy of 0.82, and an AUC of 0.83 and 0.84, respectively, which were slightly lower than that of LR using one image SVP (0.85) or two images DVP and SVP (0.85). The LR-EN and LR classification algorithms had the high sensitivity, specificity, and diagnostic accuracy in identifying DR, which may be promising in facilitating the early diagnosis of DR.Zhiping LiuChen WangXiaodong CaiHong JiangJianhua WangIEEEarticleDiabetic retinopathymachine learninglogistic regressionlogistic regression regularized with the elastic net penaltysupport vector machineElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 51689-51694 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Diabetic retinopathy machine learning logistic regression logistic regression regularized with the elastic net penalty support vector machine Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Diabetic retinopathy machine learning logistic regression logistic regression regularized with the elastic net penalty support vector machine Electrical engineering. Electronics. Nuclear engineering TK1-9971 Zhiping Liu Chen Wang Xiaodong Cai Hong Jiang Jianhua Wang Discrimination of Diabetic Retinopathy From Optical Coherence Tomography Angiography Images Using Machine Learning Methods
description	The goal was to discriminate between diabetic retinopathy (DR) and healthy controls (HC) by evaluating Optical coherence tomography angiography (OCTA) images from <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> mm scans with the assistance of different machine learning models. The OCTA angiography dataset of superficial vascular plexus (SVP), deep vascular plexus (DVP), and retinal vascular network (RVN) were acquired from 19 DR (38 eyes) patients and 25 HC (44 eyes). A discrete wavelet transform was applied to extract texture features from each image. Four machine learning models, including logistic regression (LR), logistic regression regularized with the elastic net penalty (LR-EN), support vector machine (SVM), and the gradient boosting tree named XGBoost, were used to classify wavelet features between groups. The area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and diagnostic accuracy of the classifiers were obtained. The OCTA image dataset included 114 and 132 images from DR and HC subjects, respectively. LR-EN and LR using all three images, SVP, DVP, and RVN, provided the highest sensitivity of 0.84 and specificity of 0.80, the best diagnostic accuracy of 0.82, and an AUC of 0.83 and 0.84, respectively, which were slightly lower than that of LR using one image SVP (0.85) or two images DVP and SVP (0.85). The LR-EN and LR classification algorithms had the high sensitivity, specificity, and diagnostic accuracy in identifying DR, which may be promising in facilitating the early diagnosis of DR.
format	article
author	Zhiping Liu Chen Wang Xiaodong Cai Hong Jiang Jianhua Wang
author_facet	Zhiping Liu Chen Wang Xiaodong Cai Hong Jiang Jianhua Wang
author_sort	Zhiping Liu
title	Discrimination of Diabetic Retinopathy From Optical Coherence Tomography Angiography Images Using Machine Learning Methods
title_short	Discrimination of Diabetic Retinopathy From Optical Coherence Tomography Angiography Images Using Machine Learning Methods
title_full	Discrimination of Diabetic Retinopathy From Optical Coherence Tomography Angiography Images Using Machine Learning Methods
title_fullStr	Discrimination of Diabetic Retinopathy From Optical Coherence Tomography Angiography Images Using Machine Learning Methods
title_full_unstemmed	Discrimination of Diabetic Retinopathy From Optical Coherence Tomography Angiography Images Using Machine Learning Methods
title_sort	discrimination of diabetic retinopathy from optical coherence tomography angiography images using machine learning methods
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/f84a093729d340cca6d0ff2bfd688b14
work_keys_str_mv	AT zhipingliu discriminationofdiabeticretinopathyfromopticalcoherencetomographyangiographyimagesusingmachinelearningmethods AT chenwang discriminationofdiabeticretinopathyfromopticalcoherencetomographyangiographyimagesusingmachinelearningmethods AT xiaodongcai discriminationofdiabeticretinopathyfromopticalcoherencetomographyangiographyimagesusingmachinelearningmethods AT hongjiang discriminationofdiabeticretinopathyfromopticalcoherencetomographyangiographyimagesusingmachinelearningmethods AT jianhuawang discriminationofdiabeticretinopathyfromopticalcoherencetomographyangiographyimagesusingmachinelearningmethods
_version_	1718416131411148800

Discrimination of Diabetic Retinopathy From Optical Coherence Tomography Angiography Images Using Machine Learning Methods

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