Detection of Optic Disc Abnormalities in Color Fundus Photographs Using Deep Learning. Issue 3 (September 2021)

• Record Type: Journal Article
• Authors: Liu, T. Y. Alvin; Wei, Jinchi; Zhu, Hongxi; Subramanian, Prem S.; Myung, David; Yi, Paul H.; Hui, Ferdinand K.; Unberath, Mathias; Ting, Daniel S. W.; Miller, Neil R.
• Other Names: Fraser Clare section editor.; Mollan Susan section editor.
• Is Part Of: Journal of neuro-ophthalmology. Volume 41:Issue 3(2021)
• Publication Date: 2021-09
• Subjects: Neuroophthalmology -- Periodicals; 617.7
• DOI: 10.1097/WNO.0000000000001358
• Languages: English
• ISSNs: 1070-8022

Abstract

Abstract : Background: To date, deep learning–based detection of optic disc abnormalities in color fundus photographs has mostly been limited to the field of glaucoma. However, many life-threatening systemic and neurological conditions can manifest as optic disc abnormalities. In this study, we aimed to extend the application of deep learning (DL) in optic disc analyses to detect a spectrum of nonglaucomatous optic neuropathies. Methods: Using transfer learning, we trained a ResNet-152 deep convolutional neural network (DCNN) to distinguish between normal and abnormal optic discs in color fundus photographs (CFPs). Our training data set included 944 deidentified CFPs (abnormal 364; normal 580). Our testing data set included 151 deidentified CFPs (abnormal 71; normal 80). Both the training and testing data sets contained a wide range of optic disc abnormalities, including but not limited to ischemic optic neuropathy, atrophy, compressive optic neuropathy, hereditary optic neuropathy, hypoplasia, papilledema, and toxic optic neuropathy. The standard measures of performance (sensitivity, specificity, and area under the curve of the receiver operating characteristic curve (AUC-ROC)) were used for evaluation. Results: During the 10-fold cross-validation test, our DCNN for distinguishing between normal and abnormal optic discs achieved the following mean performance: AUC-ROC 0.99 (95 CI: 0.98–0.99), sensitivity 94% (95 CI: 91%–97%), and specificity 96% (95 CI: 93%–99%). When evaluated against the external testing data set, our model achieved the following mean performance: AUC-ROC 0.87, sensitivity 90%, and specificity 69%. Conclusion: In summary, we have developed a deep learning algorithm that is capable of detecting a spectrum of optic disc abnormalities in color fundus photographs, with a focus on neuro-ophthalmological etiologies. As the next step, we plan to validate our algorithm prospectively as a focused screening tool in the emergency department, which if successful could be beneficial because current practice pattern and training predict a shortage of neuro-ophthalmologists and ophthalmologists in general in the near future. Abstract : Supplemental Digital Content is Available in the Text.