Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment

Scientific Reports

Volumn 7, Issue 1, 2017, Pages

  Ohsugi, Hideharu ^a   Tabuchi, Hitoshi ^a   Enno, Hiroki ^b   Ishitobi, Naofumi ^a  

^a Tsukazaki Hospital   (Japan)

^b RESEARCH ORGANIZATION FOR INFORMATION SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

AGED; EYE FUNDUS; FEMALE; HUMAN; MACHINE LEARNING; MALE; MIDDLE AGED; OPHTHALMOSCOPY; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; RETINA DETACHMENT; SENSITIVITY AND SPECIFICITY;

AGED; DEEP LEARNING; FEMALE; FUNDUS OCULI; HUMANS; MACHINE LEARNING; MALE; MIDDLE AGED; OPHTHALMOSCOPY; RETINAL DETACHMENT; ROC CURVE; SENSITIVITY AND SPECIFICITY;

EID: 85028359586     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-017-09891-x     Document Type: Article

References (21)

1. Miki, D., Hida, T., Hotta, K., Shinoda, K., Hirakata, A. Comparison of scleral buckling and vitrectomy for retinal detachment resulting from flap tears in superior quadrants. Jpn. J. Ophthalmol. 45, 187-191 (2001).
2. Heussen, N., et al. Scleral buckling versus primary vitrectomy in rhegmatogenous retinal detachment study (SPR Study): Predictive factors for functional outcome. Study report no. 6. Graefes Arch. Clin. Exp. Ophthalmol. 249, 1129-1136 (2011).
3. Lean, J. S., et al. Silicone Study Group. Vitrectomy with silicone oil or sulfur hexafluoride gas in eyes with severe proliferative vitreoretinopathy: Results of a randomized clinical trial. Silicone Study Report 1. Arch. Ophthalmol. 110, 770-779 (1992).
4. AZEN, S., et al. Silicone Study Group. Vitrectomy with silicone oil or sulfur hexafluoride gas in eyes with severe proliferative vitreoretinopathy: Results of a randomized clinical trial. Silicone Study Report 2. Arch. Ophthalmol. 110, 780-792 (1992).
5. Scott, I. U., Flynn, H. W. Jr., Murray, T. G., Feuer, W. J. Perfluoron study group. Outcomes of surgery for retinal detachment associated with proliferative vitreoretinopathy using perfluoro-n-octane: A multicenter study. Am. J. Ophthalmol. 136, 454-463 (2003).
6. Mrsnik, M. Global aging 2013: Rising to the challenge. Standard, poor's rating services Network https://www.nact.org/resources/2013-NACT-Global-Aging.pdf (2013).
7. Nagiel, A., Lalane, R. A., Sadda, S. R., Schwartz, S. D. Ultra-widefield fundus imaging: A review of clinical applications and future trends. Retina 36, 660-678 (2016).
8. LeCun, Y., Bengio, Y., Hinton, G. Deep learning. Nature 521, 436-444 (2015).
9. Liu, S., et al. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Trans. Biomed. Eng. 62, 1132-1140 (2015).
10. Litjens, G., et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016).
11. Gulshan, V., et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402-2410 (2016).
12. Pinaya, W. H., et al. Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Sci. Rep. 6, 38897 (2016).
13. Deng, J., et al. Imagenet: A large-scale hierarchical image database. Computer Vision and Pattern Recognition 248-255 (2009).
14. Russakovsky, O., et al. Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115, 211-252 (2015).
15. Lee, C. Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z. Deeply-supervised nets. InAISTATS 2, 5 (2015).
16. Glorot, X., Bordes, A., Bengio, Y. Deep sparse rectifier neural networks. The 14th International Conference on Artificial Intelligence and Statistics. 315-323 (2011).
17. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929-1958 (2014).
18. Duchi, J., Hazan, E., Singer, Y. Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121-2159 (2011).
19. Brereton, R. G., Lloyd, G. R. Support vector machines for classification and regression. Analyst 135, 230-267 (2010).
20. Chang, C. C., Lin, C. J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27 (2011).
21. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825-2830 (2011).