Retinal Detachment Screening with Ensembles of Neural Network Models

Rhegmatogenous retinal detachment is an important condition that should be diagnosed early. A previous study showed that normal eyes and eyes with rhegmatogenous retinal detachment could be distinguished using pseudo-ocular fundus color images obtained with the Optos camera. However, no study has used pseudo-ocular fundus color images to distinguish eyes without retinal detachment (not necessarily normal) and those with rhegmatogenous retinal detachment. Furthermore, the previous study used a single neural network with only three layers. In the current study, we trained and validated an ensemble model of a deep neural networks involving ultra-wide-field pseudocolor images to distinguish non-retinal detachment eyes (not necessarily normal) and rhegmatogenous retinal detachment eyes. The study included 600 non-retinal detachment, 693 bullous rhegmatogenous retinal detachment, and 125 non-bullous rhegmatogenous retinal detachment images. The sensitivity and specificity of the ensemble model (five models) were 97.3% and 91.5%, respectively. In sum, this study demonstrated promising results for a screening system for rhegmatogenous retinal detachment with high sensitivity and relatively high specificity.

[1]  P. Walter,et al.  Scleral buckling versus primary vitrectomy in rhegmatogenous retinal detachment study (SPR Study): predictive factors for functional outcome. Study report no. 6 , 2011, Graefe's Archive for Clinical and Experimental Ophthalmology.

[2]  A. Luff,et al.  Management of retinal detachment: a guide for non-ophthalmologists , 2008, BMJ : British Medical Journal.

[3]  Hideharu Ohsugi,et al.  Accuracy of deep learning, a machine-learning technology, using ultra–wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment , 2017, Scientific Reports.

[4]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[5]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[7]  D. Miki,et al.  Comparison of scleral buckling and vitrectomy for retinal detachment resulting from flap tears in superior quadrants. , 2001, Japanese journal of ophthalmology.

[8]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[9]  Geraint Rees,et al.  Clinically applicable deep learning for diagnosis and referral in retinal disease , 2018, Nature Medicine.

[10]  T. Aaberg,et al.  AN UPDATED CLASSIFICATION OF RETINAL DETACHMENT WITH PROLIFERATIVE VITREORETINOPATHY , 1991 .

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[13]  W. Feuer,et al.  Outcomes of surgery for retinal detachment associated with proliferative vitreoretinopathy using perfluoro-n-octane: a multicenter study. , 2002, American journal of ophthalmology.

[14]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[15]  Zhuowen Tu,et al.  Deeply-Supervised Nets , 2014, AISTATS.

[16]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[17]  Ning Qian,et al.  On the momentum term in gradient descent learning algorithms , 1999, Neural Networks.

[18]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[19]  Y. Nesterov.  A method for unconstrained convex minimization problem with the rate of convergence o(1/k^2) , 1983 .

[20]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[21]  Neil J. Joshi,et al.  Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular Degeneration , 2018, JAMA ophthalmology.

[22]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Jitendra Malik,et al.  Analyzing the Performance of Multilayer Neural Networks for Object Recognition , 2014, ECCV.

[24]  A. Peters,et al.  A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography. , 2018, Ophthalmology.

[25]  Neil J. Joshi,et al.  Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks , 2017, JAMA ophthalmology.

[26]  Enrique F Schisterman,et al.  Youden Index and the optimal threshold for markers with mass at zero , 2008, Statistics in medicine.