Identifying Diabetic Macular Edema and Other Retinal Diseases by Optical Coherence Tomography Image and Multiscale Deep Learning

References

• Torkamani A, Topol E. Polygenic risk scores expand to obesity. Cell. 2019;177(3):518–520. doi:10.1016/j.cell.2019.03.051
   PubMedGoogle Scholar
• Shenghui H, Norman E. Sharpless senescence in health and disease. Cell. 2017;169(6):1000–1011. doi:10.1016/j.cell.2017.05.015
   PubMedGoogle Scholar
• Polubriaginof FCG, Vanguri R, Quinnies K, et al. Disease heritability inferred from familial relationships reported in medical records. Cell. 2018;173(7):1692–1704. doi:10.1016/j.cell.2018.04.032
   PubMed Web of Science ®Google Scholar
• Dwolatzky T, Brodsky J, Azaiza F, et al. Coming of age: health-care challenges of an ageing population in Israel. Lancet. 2017;389(10088):2542–2550. doi:10.1016/S0140-6736(17)30789-4
   PubMed Web of Science ®Google Scholar
• Manolio TA, Rowley R, Williams MS, et al. Opportunities, resources, and techniques for implementing genomics in clinical care. Lancet. 2019;394(10197):511–520. doi:10.1016/S0140-6736(19)31140-7
   PubMedGoogle Scholar
• Abeysinghe S, Leppold C, Ozaki A, et al. Disappearing everyday materials: the displacement of medical resources following disaster in Fukushima, Japan. Soc Sci Med. 2017;191:117–124. doi:10.1016/j.socscimed.2017.09.011
   PubMedGoogle Scholar
• WHO Ebola Response Team. After ebola in West Africa — unpredictable risks, preventable epidemics. N Engl J Med. 2016;375(6):587–596. doi:10.1056/NEJMsr1513109
   PubMed Web of Science ®Google Scholar
• Mayosi BM, Benatar SR. Health and health care in South Africa — 20 years after Mandela. N Engl J Med. 2014;371(14):1344–1353. doi:10.1056/NEJMsr1405012
   PubMed Web of Science ®Google Scholar
• Chilamkurthy S, Ghosh R, Tanamala S, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 2018;392(10162):2388–2396. doi:10.1016/S0140-6736(18)31645-3
   PubMed Web of Science ®Google Scholar
• Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25(6):954–961. doi:10.1038/s41591-019-0447-x
   PubMed Web of Science ®Google Scholar
• Schwemmer Michael A, Skomrock Nicholas D, Sederberg Per B, et al. Meeting brain–computer interface user performance expectations using a deep neural network decoding framework. Nat Med. 2018;24(11):1669. doi:10.1038/s41591-018-0171-y
   PubMedGoogle Scholar
• Wang Y, Riordon J, Kong T. Prediction of DNA integrity from morphological parameters using a single‐sperm DNA fragmentation index assay. Adv Sci. 2019;6(15):1900712. doi:10.1002/advs.201900712
   Google Scholar
• Cho NH, Shaw JE, Karuranga S, et al. IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271–281. doi:10.1016/j.diabres.2018.02.023
   PubMed Web of Science ®Google Scholar
• Ting DSW, Cheung CY, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA. 2017;318(22):2211–2223. doi:10.1001/jama.2017.18152
   PubMed Web of Science ®Google Scholar
• Flaxman SR, Bourne RRA, Resnikoff S, et al. Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis. Lancet Glob Health. 2017;5(12):e1221–e1234. doi:10.1016/S2214-109X(17)30393-5
   PubMed Web of Science ®Google Scholar
• Tilahun M, Gobena T, Dereje D, et al. Prevalence of Diabetic retinopathy and its associated factors among diabetic patients at Debre Markos referral hospital, Northwest Ethiopia, 2019: hospital-based cross-sectional study. Diabetes Metab Syndr Obes. 2020;13:2179–2187. doi:10.2147/DMSO.S260694
   PubMed Web of Science ®Google Scholar
• Maturi RK, Glassman AR, Liu D, et al. Effect of adding dexamethasone to continued ranibizumab treatment in patients with persistent diabetic macular edema: a DRCR network phase 2 randomized clinical trial. JAMA Ophthalmol. 2018;136(1):29–38. doi:10.1001/jamaophthalmol.2017.4914
   PubMed Web of Science ®Google Scholar
• Shan K, Liu C, Liu B-H, et al. Circular noncoding RNA HIPK3 mediates retinal vascular dysfunction in diabetes mellitus. Circulation. 2017;136(17):1629–1642. doi:10.1161/CIRCULATIONAHA.117.029004
   PubMed Web of Science ®Google Scholar
• Sung IS, Park SY, Jeong K-Y, et al. Investigation of the preventive effect of calcium on inflammation-mediated choroidal neovascularization. Life Sci. 2019;233:116727. doi:10.1016/j.lfs.2019.116727
   PubMed Web of Science ®Google Scholar
• LeBlanc ME, Wang W, Ji Y, et al. Secretogranin III as a novel target for the therapy of choroidal neovascularization. Exp Eye Res. 2019;181:120–126. doi:10.1016/j.exer.2019.01.009
   PubMed Web of Science ®Google Scholar
• Askou AL, Alsing S, Benckendorff JNE, et al. Suppression of choroidal neovascularization by AAV-based dual-acting antiangiogenic gene therapy. Mol Ther Nucleic Acids. 2019;16:38–50. doi:10.1016/j.omtn.2019.01.012
   PubMedGoogle Scholar
• Nakagami Y, Hatano E, Chayama Y, et al. An anti-PLVAP antibody suppresses laser-induced choroidal neovascularization in monkeys. Eur J Pharmacol. 2019;854:240–246. doi:10.1016/j.ejphar.2019.04.035
   PubMedGoogle Scholar
• Bergen AA, Arya S, Koster C, et al. On the origin of proteins in human drusen: the meet, greet and stick hypothesis. Prog Retin Eye Res. 2019;70:55–84. doi:10.1016/j.preteyeres.2018.12.003
   PubMed Web of Science ®Google Scholar
• Liu L, Edmunds B, Takusagawa HL, et al. Projection-resolved optical coherence tomography angiography of the peripapillary retina in glaucoma. Am J Ophthalmol. 2019;207:99–109. doi:10.1016/j.ajo.2019.05.024
   PubMed Web of Science ®Google Scholar
• Ito Y, Sasaki M, Takahashi H, et al. Quantitative assessment of the retina using OCT and associations with cognitive function. Ophthalmology. 2020;127(1):107–118. doi:10.1016/j.ophtha.2019.05.021
   PubMedGoogle Scholar
• Sarıgül M, Ozyildirim BM, Avci M. Differential convolutional neural network. Neural Netw. 2019;116:279–287. doi:10.1016/j.neunet.2019.04.025
   PubMedGoogle Scholar
• Zhou JT, Pan SJ, Tsang IW. A deep learning framework for hybrid heterogeneous transfer learning. Artif Intell. 2019;275:310–328. doi:10.1016/j.artint.2019.06.001
   Web of Science ®Google Scholar
• de la Torre J, Valls A, Puig D. A deep learning interpretable classifier for diabetic retinopathy disease grading. Neurocomputing. 2020;396:465–476. doi:10.1016/j.neucom.2018.07.102
   Web of Science ®Google Scholar
• Girish GN, Thakur B, Chowdhury SR, et al. Segmentation of intra-retinal cysts from optical coherence tomography images using a fully convolutional neural network model. IEEE J Biomed Health Inform. 2019;23(1):296–304. doi:10.1109/JBHI.2018.2810379
   PubMedGoogle Scholar
• Niu S, Chen Q, Sisternes L, et al. Automated detection of foveal center in SD‐OCT images using the saliency of retinal thickness maps. Med Phys. 2017;44(12):6390–6403. doi:10.1002/mp.12614
   PubMedGoogle Scholar
• Fang L, Wang C, Shutao L, et al. Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels. J Biomed Opt. 2017;22(11):116011. doi:10.1117/1.JBO.22.11.116011
   Google Scholar
• Yoshioka N, Zangerl B, Nivison-Smith L, et al. Pattern recognition analysis of age-related retinal ganglion cell signatures in the human eye. Invest Ophthalmol Vis Sci. 2017;58(7):3086–3099. doi:10.1167/iovs.17-21450
   PubMedGoogle Scholar
• Chakravarthy U, Harding SP, Rogers CA, et al. Alternative treatments to inhibit VEGF in age-related choroidal neovascularisation: 2-year findings of the IVAN randomised controlled trial. Lancet. 2013;382(9900):1258–1267.
   PubMed Web of Science ®Google Scholar
• Croci DO, Cerliani JP, Dalotto-Moreno T, et al. Glycosylation-dependent lectin-receptor interactions preserve angiogenesis in anti-VEGF refractory tumors. Cell. 2014;156(4):744–758. doi:10.1016/j.cell.2014.01.043
   PubMed Web of Science ®Google Scholar
• Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122–1131.e9. doi:10.1016/j.cell.2018.02.010
   PubMed Web of Science ®Google Scholar
• Kingma DP, Jimmy B. A method for stochastic optimization. arXiv Preprint. 2014;arXiv:1412.6980.
   Google Scholar
• Xie S, Tu Z. Holistically-nested edge detection. Proc IEEE Int Conf Comput Vis. 2015;1395–1403.
   Google Scholar
• Roth HR, Lu L, Lay N, et al. Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. Med Image Anal. 2018;45:94–107. doi:10.1016/j.media.2018.01.006
   PubMedGoogle Scholar
• Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision. arXiv Preprint. 2016;arXiv:1512.00567.
   Google Scholar
• Liu W, Wen Y, Zhiding Y, et al. Large-margin softmax loss for convolutional neural networks. ICML. 2016;2(3):7.
   Google Scholar
• Perdomo O, Rios H, Rodríguez FJ, et al. Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography. Comput Methods Programs Biomed. 2019;178:181–189. doi:10.1016/j.cmpb.2019.06.016
   PubMed Web of Science ®Google Scholar
• Pratap T, Kokil P. Computer-aided diagnosis of cataract using deep transfer learning. Biomed Signal Process Control. 2019;53:101533. doi:10.1016/j.bspc.2019.04.010
   Web of Science ®Google Scholar
• Tao L, Gao Y, Wang K, et al. Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Inf Sci (Ny). 2019;501:511–522. doi:10.1016/j.ins.2019.06.011
   Google Scholar
• Sayres R, Taly A, Rahimy E, et al. Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy. Ophthalmology. 2019;126(4):552–564. doi:10.1016/j.ophtha.2018.11.016
   PubMed Web of Science ®Google Scholar
• Bellemo V, Lim ZW, Lim G, et al. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. Lancet Digit Health. 2019;1(1):e35–e44. doi:10.1016/S2589-7500(19)30004-4
   PubMedGoogle Scholar
• Arcadu F, Benmansour F, Maunz A, et al. Deep learning predicts OCT measures of diabetic macular thickening from color fundus photographs. Invest Ophthalmol Vis Sci. 2019;60(4):852–857. doi:10.1167/iovs.18-25634
   PubMed Web of Science ®Google Scholar
• Chan GCY, Muhammad A, Shah SAA, et al. Transfer learning for diabetic macular edema (DME) detection on optical coherence tomography (OCT) images. 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA); 2017; Kuching. 493–496. doi: 10.1109/ICSIPA.2017.8120662.
   Google Scholar
• Awais M, Müller H, Tang TB, et al. Classification of SD-OCT images using a deep learning approach. 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA); 2017; Kuching. 489–492. doi: 10.1109/ICSIPA.2017.8120661.
   Google Scholar
• Schlegl T, Waldstein SM, Bogunovic H, et al. Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology. 2018;125(4):549–558. doi:10.1016/j.ophtha.2017.10.031
   PubMed Web of Science ®Google Scholar
• Liu Y, Liu G, Zhang Q. Deep learning and medical diagnosis. Lancet. 2019;394(10210):1709–1710. doi:10.1016/S0140-6736(19)32501-2
   PubMed Web of Science ®Google Scholar