Deep Learning-Based Noise Reduction Improves Optical Coherence Tomography Angiography Imaging of Radial Peripapillary Capillaries in Advanced Glaucoma

References

• Nakazawa T. Ocular blood flow and influencing factors for glaucoma. Asia Pac J Ophthalmol. 2016;5:38–44.
   PubMed Web of Science ®Google Scholar
• Nakazawa T, Fukuchi T. What is glaucomatous optic neuropathy? Jpn J Ophthalmol. 2020;64(3):243–249. doi:10.1007/s10384-020-00736-1.
   PubMed Web of Science ®Google Scholar
• Iwase A, Suzuki Y, Araie M, Yamamoto T, Abe H, Shirato S, Kuwayama Y, Mishima HK, Shimizu H, Tomita G, Tajimi Study Group, Japan Glaucoma Society, et al. The prevalence of primary open-angle glaucoma in Japanese: the Tajimi Study. Ophthalmology. 2004;111(9):1641–1648. doi:10.1016/j.ophtha.2004.03.029.
   PubMed Web of Science ®Google Scholar
• Yokoyama Y, Maruyama K, Konno H, Hashimoto S, Takahashi M, Kayaba H, Kokubun T, Nakazawa T. Characteristics of patients with primary open angle glaucoma and normal tension glaucoma at a university hospital: a cross-sectional retrospective study. BMC Res Notes. 2015;8(1):360. doi:10.1186/s13104-015-1339-x.
   PubMedGoogle Scholar
• Caprioli J, Coleman AL, Blood Flow in Glaucoma Discussion Blood Flow in Glaucoma D. Blood pressure, perfusion pressure, and glaucoma. Am J Ophthalmol. 2010;149(5):704–712. doi:10.1016/j.ajo.2010.01.018.
   PubMed Web of Science ®Google Scholar
• Leske MC. Ocular perfusion pressure and glaucoma: clinical trial and epidemiologic findings. Curr Opin Ophthalmol. 2009;20(2):73–78. doi:10.1097/ICU.0b013e32831eef82.
   PubMed Web of Science ®Google Scholar
• Ernest PJ, Schouten JS, Beckers HJ, Hendrikse F, Prins MH, Webers CA. An evidence-based review of prognostic factors for glaucomatous visual field progression. Ophthalmology. 2013;120(3):512–519. doi:10.1016/j.ophtha.2012.09.005.
   PubMed Web of Science ®Google Scholar
• Wey S, Amanullah S, Spaeth GL, Ustaoglu M, Rahmatnejad K, Katz LJ. Is primary open-angle glaucoma an ocular manifestation of systemic disease? Graefes Arch Clin Exp Ophthalmol. 2019;257(4):665–673. doi:10.1007/s00417-019-04239-9.
   PubMed Web of Science ®Google Scholar
• Kiyota N, Kunikata H, Shiga Y, Omodaka K, Nakazawa T. Relationship between laser speckle flowgraphy and optical coherence tomography angiography measurements of ocular microcirculation. Graefes Arch Clin Exp Ophthalmol. 2017;255(8):1633–1642. doi:10.1007/s00417-017-3627-8.
   PubMed Web of Science ®Google Scholar
• Shiga Y, Omodaka K, Kunikata H, Ryu M, Yokoyama Y, Tsuda S, Asano T, Maekawa S, Maruyama K, Nakazawa T. Waveform analysis of ocular blood flow and the early detection of normal tension glaucoma. Invest Ophthalmol Vis Sci. 2013;54(12):7699–7706. doi:10.1167/iovs.13-12930.
   PubMed Web of Science ®Google Scholar
• Shiga Y, Kunikata H, Aizawa N, Kiyota N, Maiya Y, Yokoyama Y, Omodaka K, Takahashi H, Yasui T, Kato K, et al. Optic nerve head blood flow, as measured by laser speckle flowgraphy, is significantly reduced in preperimetric glaucoma. Curr Eye Res. 2016;41(11):1447–1453. doi:10.3109/02713683.2015.1127974.
   PubMed Web of Science ®Google Scholar
• Omodaka K, Fujioka S, An G, Udagawa T, Tsuda S, Shiga Y, Morishita S, Kikawa T, Pak K, Akiba M, et al. Structural characterization of glaucoma patients with low ocular blood flow. Curr Eye Res. 2020;45(10):1302–1308.
   PubMed Web of Science ®Google Scholar
• Shiga Y, Aizawa N, Tsuda S, Yokoyama Y, Omodaka K, Kunikata H, Yasui T, Kato K, Kurashima H, Miyamoto E, et al. Preperimetric Glaucoma Prospective Study (PPGPS): predicting visual field progression with basal optic nerve head blood flow in normotensive PPG eyes. Transl Vis Sci Technol. 2018;7(1):11. doi:10.1167/tvst.7.1.11.
   PubMed Web of Science ®Google Scholar
• Yarmohammadi A, Zangwill LM, Diniz-Filho A, Suh MH, Yousefi S, Saunders LJ, Belghith A, Manalastas PI, Medeiros FA, Weinreb RN. Relationship between optical coherence tomography angiography vessel density and severity of visual field loss in glaucoma. Ophthalmology. 2016;123(12):2498–2508. doi:10.1016/j.ophtha.2016.08.041.
   PubMed Web of Science ®Google Scholar
• Yarmohammadi A, Zangwill LM, Diniz-Filho A, Suh MH, Manalastas PI, Fatehee N, Yousefi S, Belghith A, Saunders LJ, Medeiros FA, et al. Optical coherence tomography angiography vessel density in healthy, glaucoma suspect, and glaucoma eyes. Invest Ophthalmol Vis Sci. 2016;57(9):OCT451–459. doi:10.1167/iovs.15-18944.
   PubMed Web of Science ®Google Scholar
• Akagi T, Iida Y, Nakanishi H, Terada N, Morooka S, Yamada H, Hasegawa T, Yokota S, Yoshikawa M, Yoshimura N. Microvascular density in glaucomatous eyes with hemifield visual field defects: an optical coherence tomography angiography study. Am J Ophthalmol. 2016;168:237–249. doi:10.1016/j.ajo.2016.06.009.
   PubMed Web of Science ®Google Scholar
• Moghimi S, Zangwill LM, Penteado RC, Hasenstab K, Ghahari E, Hou H, Christopher M, Yarmohammadi A, Manalastas PIC, Shoji T, et al. Macular and optic nerve head vessel density and progressive retinal nerve fiber layer loss in glaucoma. Ophthalmology. 2018;125(11):1720–1728. doi:10.1016/j.ophtha.2018.05.006.
   PubMed Web of Science ®Google Scholar
• Kiyota N, Kunikata H, Takahashi S, Shiga Y, Omodaka K, Nakazawa T. Factors associated with deep circulation in the peripapillary chorioretinal atrophy zone in normal-tension glaucoma with myopic disc. Acta Ophthalmol. 2018;96(3):e290–e297. doi:10.1111/aos.13621.
   PubMed Web of Science ®Google Scholar
• Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410. doi:10.1001/jama.2016.17216.
   PubMed Web of Science ®Google Scholar
• Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122–1131 e1129. doi:10.1016/j.cell.2018.02.010.
   PubMed Web of Science ®Google Scholar
• De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, Askham H, Glorot X, O'Donoghue B, Visentin D, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342–1350. doi:10.1038/s41591-018-0107-6.
   PubMed Web of Science ®Google Scholar
• Asaoka R, Hirasawa K, Iwase A, Fujino Y, Murata H, Shoji N, Araie M. Validating the usefulness of the "random forests" classifier to diagnose early glaucoma with optical coherence tomography. Am J Ophthalmol. 2017;174:95–103. doi:10.1016/j.ajo.2016.11.001.
   PubMed Web of Science ®Google Scholar
• Omodaka K, An G, Tsuda S, Shiga Y, Takada N, Kikawa T, Takahashi H, Yokota H, Akiba M, Nakazawa T. Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters. PLOS One. 2017;12(12):e0190012. doi:10.1371/journal.pone.0190012.
   PubMed Web of Science ®Google Scholar
• An G, Omodaka K, Hashimoto K, Tsuda S, Shiga Y, Takada N, Kikawa T, Yokota H, Akiba M, Nakazawa T. Glaucoma diagnosis with machine learning based on optical coherence tomography and color fundus images. J Healthc Eng. 2019;2019:4061313. doi:10.1155/2019/4061313.
   PubMed Web of Science ®Google Scholar
• Mursch-Edlmayr AS, Ng WS, Diniz-Filho A, Sousa DC, Arnold L, Schlenker MB, Duenas-Angeles K, Keane PA, Crowston JG, Jayaram H. Artificial intelligence algorithms to diagnose glaucoma and detect glaucoma progression: translation to clinical practice. Transl Vis Sci Technol. 2020;9(2):55. doi:10.1167/tvst.9.2.55.
   PubMed Web of Science ®Google Scholar
• Maetschke S, Antony B, Ishikawa H, Wollstein G, Schuman J, Garnavi R. A feature agnostic approach for glaucoma detection in OCT volumes. PLOS One. 2019;14(7):e0219126. doi:10.1371/journal.pone.0219126.
   PubMed Web of Science ®Google Scholar
• Kawai K, Uji A, Murakami T, Kadomoto S, Oritani Y, Dodo Y, Muraoka Y, Akagi T, Miyata M, Tsujikawa A. Image evaluation of artificial intelligence-supported optical coherence tomography angiography imaging using Oct-A1 device in diabetic retinopathy. Retina. 2021;41(8):1730–1738. doi:10.1097/IAE.0000000000003101.
   PubMed Web of Science ®Google Scholar
• Kim AY, Chu Z, Shahidzadeh A, Wang RK, Puliafito CA, Kashani AH. Quantifying microvascular density and morphology in diabetic retinopathy using spectral-domain optical coherence tomography angiography. Invest Ophthalmol Vis Sci. 2016;57(9):OCT362–370. doi:10.1167/iovs.15-18904.
   PubMed Web of Science ®Google Scholar
• Jerman T, Pernus F, Likar B, Spiclin Z. Enhancement of vascular structures in 3D and 2D angiographic images. IEEE Trans Med Imaging. 2016;35(9):2107–2118. doi:10.1109/TMI.2016.2550102.
   PubMed Web of Science ®Google Scholar
• An G, Omodaka K, Tsuda S, Shiga Y, Takada N, Kikawa T, Nakazawa T, Yokota H, Akiba M. Comparison of machine-learning classification models for glaucoma management. J Healthc Eng. 2018;2018:6874765. doi:10.1155/2018/6874765.
   PubMed Web of Science ®Google Scholar
• Uji A, Balasubramanian S, Lei J, Baghdasaryan E, Al-Sheikh M, Sadda SR. Impact of multiple en face image averaging on quantitative assessment from optical coherence tomography angiography images. Ophthalmology. 2017;124(7):944–952. doi:10.1016/j.ophtha.2017.02.006.
   PubMed Web of Science ®Google Scholar
• Murakawa S, Maruko I, Kawano T, Hasegawa T, Iida T. Choroidal neovascularization imaging using multiple en face optical coherence tomography angiography image averaging. Graefes Arch Clin Exp Ophthalmol. 2019;257(6):1119–1125. doi:10.1007/s00417-019-04275-5.
   PubMed Web of Science ®Google Scholar
• Lauermann JL, Xu Y, Heiduschka P, Treder M, Alten F, Eter N, Alnawaiseh M. Impact of integrated multiple image averaging on OCT angiography image quality and quantitative parameters. Graefes Arch Clin Exp Ophthalmol. 2019;257(12):2623–2629. doi:10.1007/s00417-019-04492-y.
   PubMed Web of Science ®Google Scholar