Abstract

As the prevalence of diabetic retinopathy (DR) continues to rise, there is a need to develop computer-aided screening methods. The current study reports and validates an ordinary least squares (OLS) method to model optical coherence tomography angiography (OCTA) images and derive OLS parameters for classifying proliferative DR (PDR) and no/mild non-proliferative DR (NPDR) from non-diabetic subjects. OLS parameters were correlated with vessel metrics quantified from OCTA images and were used to determine predicted probabilities of PDR, no/mild NPDR, and non-diabetics. The classification rates of PDR and no/mild NPDR from non-diabetic subjects were 94% and 91%, respectively. The method had excellent predictive ability and was validated. With further development, the method may have potential clinical utility and contribute to image-based computer-aided screening and classification of stages of DR and other ocular and systemic diseases.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (10)

C. C. Hsiao, H. M. Hsu, C. M. Yang, and C. H. Yang, “Correlation of retinal vascular perfusion density with dark adaptation in diabetic retinopathy,” Graefe's Arch. Clin. Exp. Ophthalmol. 257(7), 1401–1410 (2019).
[Crossref]

T. Nazir, A. Irtaza, Z. Shabbir, A. Javed, U. Akram, and M. T. Mahmood, “Diabetic retinopathy detection through novel tetragonal local octa patterns and extreme learning machines,” Artif. Intell. Med. 99, 101695 (2019).
[Crossref]

M. Alam, D. Le, J. I. Lim, R. V. Chan, and X. Yao, “Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies,” J. Clin. Med. 8(6), 872 (2019).
[Crossref]

S. Long, X. Huang, Z. Chen, S. Pardhan, and D. Zheng, “Automatic Detection of Hard Exudates in Color Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation,” BioMed Res. Int. 2019, 3926930 (2019).
[Crossref]

K. Balasubramanian and N. P. Ananthamoorthy, “Analysis of hybrid statistical textural and intensity features to discriminate retinal abnormalities through classifiers,” Proc. Inst. Mech. Eng., Part H 233(5), 506–514 (2019).
[Crossref]

M. M. Khansari, W. D. O’Neill, R. D. Penn, N. P. Blair, and M. Shahidi, “Detection of Subclinical Diabetic Retinopathy by Fine Structure Analysis of Retinal Images,” J. Ophthalmol. 2019, 1–6 (2019).
[Crossref]

F. Li, Z. Liu, H. Chen, M. Jiang, X. Zhang, and Z. Wu, “Automatic Detection of Diabetic Retinopathy in Retinal Fundus Photographs Based on Deep Learning Algorithm,” Trans. Vis. Sci. Tech. 8(6), 4 (2019).
[Crossref]

N. Salamat, M. M. S. Missen, and A. Rashid, “Diabetic retinopathy techniques in retinal images: A review,” Artif. Intell. Med. 97, 168–188 (2019).
[Crossref]

J. Sahlsten, J. Jaskari, J. Kivinen, L. Turunen, E. Jaanio, K. Hietala, and K. Kaski, “Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading,” Sci. Rep. 9(1), 10750 (2019).
[Crossref]

E. J. Topol, “High-performance medicine: the convergence of human and artificial intelligence,” Nat. Med. 25(1), 44–56 (2019).
[Crossref]

2018 (13)

J. Krause, V. Gulshan, E. Rahimy, P. Karth, K. Widner, G. S. Corrado, L. Peng, and D. R. Webster, “Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy,” Ophthalmology 125(8), 1264–1272 (2018).
[Crossref]

M. F. Norgaard and J. Grauslund, “Automated Screening for Diabetic Retinopathy - A Systematic Review,” Ophthalmic Res. 60(1), 9–17 (2018).
[Crossref]

T. L. Torp, R. Kawasaki, T. Y. Wong, T. Peto, and J. Grauslund, “Temporal changes in retinal vascular parameters associated with successful panretinal photocoagulation in proliferative diabetic retinopathy: A prospective clinical interventional study,” Acta Ophthalmol. 96(4), 405–410 (2018).
[Crossref]

J. Lei, E. Yi, Y. Suo, C. Chen, X. Xu, W. Ding, N. S. Abdelfattah, X. Fan, and H. Lu, “Distinctive Analysis of Macular Superficial Capillaries and Large Vessels Using Optical Coherence Tomographic Angiography in Healthy and Diabetic Eyes,” Invest. Ophthalmol. Visual Sci. 59(5), 1937–1943 (2018).
[Crossref]

R. Rajalakshmi, R. Subashini, R. M. Anjana, and V. Mohan, “Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence,” Eye 32(6), 1138–1144 (2018).
[Crossref]

H. S. Sandhu, N. Eladawi, M. Elmogy, R. Keynton, O. Helmy, S. Schaal, and A. El-Baz, “Automated diabetic retinopathy detection using optical coherence tomography angiography: a pilot study,” Br. J. Ophthalmol. 102(11), 1564–1569 (2018).
[Crossref]

M. Alam, Y. Zhang, J. I. Lim, R. V. P. Chan, M. Yang, and X. Yao, “Quantitative optical coherence tomography angiography features for objective classification and staging of diabetic retinopathy,” Retina 40, 1 (2018).
[Crossref]

S. S. Kar and S. P. Maity, “Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy,” IEEE Trans. Biomed. Eng. 65(3), 608–618 (2018).
[Crossref]

P. Chudzik, S. Majumdar, F. Caliva, B. Al-Diri, and A. Hunter, “Microaneurysm detection using fully convolutional neural networks,” Comput. Meth. Prog. Bio. 158, 185–192 (2018).
[Crossref]

P. Porwal, S. Pachade, M. Kokare, L. Giancardo, and F. Meriaudeau, “Retinal image analysis for disease screening through local tetra patterns,” Comput. Biol. Med. 102, 200–210 (2018).
[Crossref]

Z. Li, S. Keel, C. Liu, Y. He, W. Meng, J. Scheetz, P. Y. Lee, J. Shaw, D. Ting, T. Y. Wong, H. Taylor, R. Chang, and M. He, “An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs,” Diabetes Care 41(12), 2509–2516 (2018).
[Crossref]

Y. Kanagasingam, D. Xiao, J. Vignarajan, A. Preetham, M. L. Tay-Kearney, and A. Mehrotra, “Evaluation of Artificial Intelligence-Based Grading of Diabetic Retinopathy in Primary Care,” JAMA Netw. Open 1(5), e182665 (2018).
[Crossref]

H. Lee, M. Lee, H. Chung, and H. C. Kim, “Quantification of Retinal Vessel Tortuosity In Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Retina 38(5), 976–985 (2018).
[Crossref]

2017 (3)

R. Gargeya and T. Leng, “Automated Identification of Diabetic Retinopathy Using Deep Learning,” Ophthalmology 124(7), 962–969 (2017).
[Crossref]

D. S. W. Ting, C. Y. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, E. Y. M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N. C. Tan, E. A. Finkelstein, E. L. Lamoureux, I. Y. Wong, N. M. Bressler, S. Sivaprasad, R. Varma, J. B. Jonas, M. G. He, C. Y. Cheng, G. C. M. Cheung, T. Aung, W. Hsu, M. L. Lee, and T. Y. Wong, “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes,” JAMA 318(22), 2211–2223 (2017).
[Crossref]

M. M. Khansari, W. O’Neill, J. Lim, and M. Shahidi, “Method for quantitative assessment of retinal vessel tortuosity in optical coherence tomography angiography applied to sickle cell retinopathy,” Biomed. Opt. Express 8(8), 3796–3806 (2017).
[Crossref]

2016 (10)

D. Bhanushali, N. Anegondi, S. G. Gadde, P. Srinivasan, L. Chidambara, N. K. Yadav, and A. Sinha Roy, “Linking Retinal Microvasculature Features With Severity of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT519 (2016).
[Crossref]

S. G. Gadde, N. Anegondi, D. Bhanushali, L. Chidambara, N. K. Yadav, A. Khurana, and A. Sinha Roy, “Quantification of Vessel Density in Retinal Optical Coherence Tomography Angiography Images Using Local Fractal Dimension,” Invest. Ophthalmol. Visual Sci. 57(1), 246–252 (2016).
[Crossref]

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Visual Sci. 57(13), 5200–5206 (2016).
[Crossref]

M. M. Khansari, W. O’Neill, R. Penn, F. Chau, N. P. Blair, and M. Shahidi, “Automated fine structure image analysis method for discrimination of diabetic retinopathy stage using conjunctival microvasculature images,” Biomed. Opt. Express 7(7), 2597–2606 (2016).
[Crossref]

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref]

A. W. Stitt, T. M. Curtis, M. Chen, R. J. Medina, G. J. McKay, A. Jenkins, T. A. Gardiner, T. J. Lyons, H. P. Hammes, R. Simo, and N. Lois, “The progress in understanding and treatment of diabetic retinopathy,” Prog. Retinal Eye Res. 51, 156–186 (2016).
[Crossref]

R. A. Gangwani, J. X. Lian, S. M. McGhee, D. Wong, and K. K. Li, “Diabetic retinopathy screening: global and local perspective,” Hong Kong Med. J. 22, 486–495 (2016).
[Crossref]

E. D. Cole, E. A. Novais, R. N. Louzada, and N. K. Waheed, “Contemporary retinal imaging techniques in diabetic retinopathy: a review,” Clin. Exp. Ophthalmol. 44(4), 289–299 (2016).
[Crossref]

J. Lee and R. Rosen, “Optical Coherence Tomography Angiography in Diabetes,” Curr. Diabetes Rep. 16(12), 123 (2016).
[Crossref]

T. Y. Wong, C. M. G. Cheung, M. Larsen, S. Sharma, and R. Simó, “Diabetic retinopathy,” Nat. Rev. Dis. Primers 2(1), 16012 (2016).
[Crossref]

2015 (5)

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitr. 1(1), 5 (2015).
[Crossref]

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2012 (1)

Y. Zheng, M. He, and N. Congdon, “The worldwide epidemic of diabetic retinopathy,” Indian J. Ophthalmol. 60(5), 428–431 (2012).
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2011 (1)

M. B. Sasongko, T. Y. Wong, T. T. Nguyen, C. Y. Cheung, J. E. Shaw, and J. J. Wang, “Retinal vascular tortuosity in persons with diabetes and diabetic retinopathy,” Diabetologia 54(9), 2409–2416 (2011).
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2010 (2)

N. Cheung, P. Mitchell, and T. Y. Wong, “Diabetic retinopathy,” Lancet 376(9735), 124–136 (2010).
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E. W. Steyerberg, A. J. Vickers, N. R. Cook, T. Gerds, M. Gonen, N. Obuchowski, M. J. Pencina, and M. W. Kattan, “Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures,” Epidemiology 21(1), 128 (2010).
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2009 (1)

U. R. Acharya, C. M. Lim, E. Y. Ng, C. Chee, and T. Tamura, “Computer-based detection of diabetes retinopathy stages using digital fundus images,” Proc. Inst. Mech. Eng., Part H 223(5), 545–553 (2009).
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2004 (2)

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2003 (1)

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2002 (1)

R. Klein, A. R. Sharrett, B. E. Klein, S. E. Moss, A. R. Folsom, T. Y. Wong, F. L. Brancati, L. D. Hubbard, and D. Couper, “The association of atherosclerosis, vascular risk factors, and retinopathy in adults with diabetes : the atherosclerosis risk in communities study,” Ophthalmology 109(7), 1225–1234 (2002).
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2000 (1)

Y. Aso, T. Inukai, K. Tayama, and Y. Takemura, “Serum concentrations of advanced glycation endproducts are associated with the development of atherosclerosis as well as diabetic microangiopathy in patients with type 2 diabetes,” Acta Diabetol. 37(2), 87–92 (2000).
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1995 (1)

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1984 (1)

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1980 (1)

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1977 (1)

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U. R. Acharya, C. M. Lim, E. Y. Ng, C. Chee, and T. Tamura, “Computer-based detection of diabetes retinopathy stages using digital fundus images,” Proc. Inst. Mech. Eng., Part H 223(5), 545–553 (2009).
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D. S. W. Ting, C. Y. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, E. Y. M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N. C. Tan, E. A. Finkelstein, E. L. Lamoureux, I. Y. Wong, N. M. Bressler, S. Sivaprasad, R. Varma, J. B. Jonas, M. G. He, C. Y. Cheng, G. C. M. Cheung, T. Aung, W. Hsu, M. L. Lee, and T. Y. Wong, “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes,” JAMA 318(22), 2211–2223 (2017).
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K. Balasubramanian and N. P. Ananthamoorthy, “Analysis of hybrid statistical textural and intensity features to discriminate retinal abnormalities through classifiers,” Proc. Inst. Mech. Eng., Part H 233(5), 506–514 (2019).
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D. S. W. Ting, C. Y. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, E. Y. M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N. C. Tan, E. A. Finkelstein, E. L. Lamoureux, I. Y. Wong, N. M. Bressler, S. Sivaprasad, R. Varma, J. B. Jonas, M. G. He, C. Y. Cheng, G. C. M. Cheung, T. Aung, W. Hsu, M. L. Lee, and T. Y. Wong, “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes,” JAMA 318(22), 2211–2223 (2017).
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G. Panozzo, B. Parolini, E. Gusson, A. Mercanti, S. Pinackatt, G. Bertoldo, and S. Pignatto, “Diabetic macular edema: an OCT-based classification,” Semin. Ophthalmol. 19(1-2), 13–20 (2004).
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D. Bhanushali, N. Anegondi, S. G. Gadde, P. Srinivasan, L. Chidambara, N. K. Yadav, and A. Sinha Roy, “Linking Retinal Microvasculature Features With Severity of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT519 (2016).
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S. G. Gadde, N. Anegondi, D. Bhanushali, L. Chidambara, N. K. Yadav, A. Khurana, and A. Sinha Roy, “Quantification of Vessel Density in Retinal Optical Coherence Tomography Angiography Images Using Local Fractal Dimension,” Invest. Ophthalmol. Visual Sci. 57(1), 246–252 (2016).
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M. M. Khansari, W. D. O’Neill, R. D. Penn, N. P. Blair, and M. Shahidi, “Detection of Subclinical Diabetic Retinopathy by Fine Structure Analysis of Retinal Images,” J. Ophthalmol. 2019, 1–6 (2019).
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D. S. W. Ting, C. Y. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, E. Y. M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N. C. Tan, E. A. Finkelstein, E. L. Lamoureux, I. Y. Wong, N. M. Bressler, S. Sivaprasad, R. Varma, J. B. Jonas, M. G. He, C. Y. Cheng, G. C. M. Cheung, T. Aung, W. Hsu, M. L. Lee, and T. Y. Wong, “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes,” JAMA 318(22), 2211–2223 (2017).
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C. Rushing, A. Bulusu, H. Hurwitz, A. B. Nixon, and H. Pang, “A leave-one-out cross validation SAS macro for the identification of markers associated with survival,” Comput. Biol. Med. 57, 123–129 (2015).
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P. Chudzik, S. Majumdar, F. Caliva, B. Al-Diri, and A. Hunter, “Microaneurysm detection using fully convolutional neural networks,” Comput. Meth. Prog. Bio. 158, 185–192 (2018).
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Cawley, G. C.

G. C. Cawley and N. L. Talbot, “Fast exact leave-one-out cross-validation of sparse least-squares support vector machines,” Neural Netw 17(10), 1467–1475 (2004).
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Chan, R. V.

M. Alam, D. Le, J. I. Lim, R. V. Chan, and X. Yao, “Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies,” J. Clin. Med. 8(6), 872 (2019).
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Chan, R. V. P.

M. Alam, Y. Zhang, J. I. Lim, R. V. P. Chan, M. Yang, and X. Yao, “Quantitative optical coherence tomography angiography features for objective classification and staging of diabetic retinopathy,” Retina 40, 1 (2018).
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Z. Li, S. Keel, C. Liu, Y. He, W. Meng, J. Scheetz, P. Y. Lee, J. Shaw, D. Ting, T. Y. Wong, H. Taylor, R. Chang, and M. He, “An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs,” Diabetes Care 41(12), 2509–2516 (2018).
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Chee, C.

U. R. Acharya, C. M. Lim, E. Y. Ng, C. Chee, and T. Tamura, “Computer-based detection of diabetes retinopathy stages using digital fundus images,” Proc. Inst. Mech. Eng., Part H 223(5), 545–553 (2009).
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Chen, C.

J. Lei, E. Yi, Y. Suo, C. Chen, X. Xu, W. Ding, N. S. Abdelfattah, X. Fan, and H. Lu, “Distinctive Analysis of Macular Superficial Capillaries and Large Vessels Using Optical Coherence Tomographic Angiography in Healthy and Diabetic Eyes,” Invest. Ophthalmol. Visual Sci. 59(5), 1937–1943 (2018).
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F. Li, Z. Liu, H. Chen, M. Jiang, X. Zhang, and Z. Wu, “Automatic Detection of Diabetic Retinopathy in Retinal Fundus Photographs Based on Deep Learning Algorithm,” Trans. Vis. Sci. Tech. 8(6), 4 (2019).
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S. Long, X. Huang, Z. Chen, S. Pardhan, and D. Zheng, “Automatic Detection of Hard Exudates in Color Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation,” BioMed Res. Int. 2019, 3926930 (2019).
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D. S. W. Ting, C. Y. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, E. Y. M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N. C. Tan, E. A. Finkelstein, E. L. Lamoureux, I. Y. Wong, N. M. Bressler, S. Sivaprasad, R. Varma, J. B. Jonas, M. G. He, C. Y. Cheng, G. C. M. Cheung, T. Aung, W. Hsu, M. L. Lee, and T. Y. Wong, “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes,” JAMA 318(22), 2211–2223 (2017).
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M. B. Sasongko, T. Y. Wong, T. T. Nguyen, C. Y. Cheung, J. E. Shaw, and J. J. Wang, “Retinal vascular tortuosity in persons with diabetes and diabetic retinopathy,” Diabetologia 54(9), 2409–2416 (2011).
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D. S. W. Ting, C. Y. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, E. Y. M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N. C. Tan, E. A. Finkelstein, E. L. Lamoureux, I. Y. Wong, N. M. Bressler, S. Sivaprasad, R. Varma, J. B. Jonas, M. G. He, C. Y. Cheng, G. C. M. Cheung, T. Aung, W. Hsu, M. L. Lee, and T. Y. Wong, “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes,” JAMA 318(22), 2211–2223 (2017).
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Cheung, N.

N. Cheung, P. Mitchell, and T. Y. Wong, “Diabetic retinopathy,” Lancet 376(9735), 124–136 (2010).
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Chidambara, L.

D. Bhanushali, N. Anegondi, S. G. Gadde, P. Srinivasan, L. Chidambara, N. K. Yadav, and A. Sinha Roy, “Linking Retinal Microvasculature Features With Severity of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT519 (2016).
[Crossref]

S. G. Gadde, N. Anegondi, D. Bhanushali, L. Chidambara, N. K. Yadav, A. Khurana, and A. Sinha Roy, “Quantification of Vessel Density in Retinal Optical Coherence Tomography Angiography Images Using Local Fractal Dimension,” Invest. Ophthalmol. Visual Sci. 57(1), 246–252 (2016).
[Crossref]

Chudzik, P.

P. Chudzik, S. Majumdar, F. Caliva, B. Al-Diri, and A. Hunter, “Microaneurysm detection using fully convolutional neural networks,” Comput. Meth. Prog. Bio. 158, 185–192 (2018).
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Chung, H.

H. Lee, M. Lee, H. Chung, and H. C. Kim, “Quantification of Retinal Vessel Tortuosity In Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Retina 38(5), 976–985 (2018).
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M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Visual Sci. 57(13), 5200–5206 (2016).
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Y. Zheng, M. He, and N. Congdon, “The worldwide epidemic of diabetic retinopathy,” Indian J. Ophthalmol. 60(5), 428–431 (2012).
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E. W. Steyerberg, A. J. Vickers, N. R. Cook, T. Gerds, M. Gonen, N. Obuchowski, M. J. Pencina, and M. W. Kattan, “Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures,” Epidemiology 21(1), 128 (2010).
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R. Klein, A. R. Sharrett, B. E. Klein, S. E. Moss, A. R. Folsom, T. Y. Wong, F. L. Brancati, L. D. Hubbard, and D. Couper, “The association of atherosclerosis, vascular risk factors, and retinopathy in adults with diabetes : the atherosclerosis risk in communities study,” Ophthalmology 109(7), 1225–1234 (2002).
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C. P. Wilkinson, F. L. Ferris, R. E. Klein, P. P. Lee, C. D. Agardh, M. Davis, D. Dills, A. Kampik, R. Pararajasegaram, and J. T. Verdaguer, “Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales,” Ophthalmology 110(9), 1677–1682 (2003).
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T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitr. 1(1), 5 (2015).
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C. P. Wilkinson, F. L. Ferris, R. E. Klein, P. P. Lee, C. D. Agardh, M. Davis, D. Dills, A. Kampik, R. Pararajasegaram, and J. T. Verdaguer, “Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales,” Ophthalmology 110(9), 1677–1682 (2003).
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J. Lei, E. Yi, Y. Suo, C. Chen, X. Xu, W. Ding, N. S. Abdelfattah, X. Fan, and H. Lu, “Distinctive Analysis of Macular Superficial Capillaries and Large Vessels Using Optical Coherence Tomographic Angiography in Healthy and Diabetic Eyes,” Invest. Ophthalmol. Visual Sci. 59(5), 1937–1943 (2018).
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T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitr. 1(1), 5 (2015).
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Figures (2)

Fig. 1.
Fig. 1. Optical coherence tomography (OCTA) images and ordinary least squares (OLS) image models of a non-diabetic (NC) (top) and proliferative diabetic retinopathy (PDR) (bottom) subject. The color bar represents pixel intensities. The OLS image model captured about 86% of the OCTA image information, hence the difference in pixel intensities between the two images.
Fig. 2.
Fig. 2. Optical coherence tomography angiography (OCTA) and fractal dimension ratio (FDR) images of a non-diabetic (NC) (top) and proliferative diabetic retinopathy (PDR) (bottom) subject. The color bar represents FDR values.

Tables (3)

Tables Icon

Table 1. Mean vessel metrics ± standard deviation in non-diabetic and PDRa groups.

Tables Icon

Table 2. Relationships between OLSa parameters and vessel metrics of PDR and non-diabetic groups

Tables Icon

Table 3. OLSa parameters associated with presence of PDRb

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

y ( i , j ) = k = 0 p l = 0 q β k l y ( i k , j l ) + u ( i , j )
v e c ( t = 1 k c t A t ) = t = 1 k c t v e c ( A t )
y 0 = [ x 1 x 2 . x p + q + q p ] β + u 0 = X β + u 0
y 0 = X b + e 0