Abstract
As the application of deep learning (DL) advances in the healthcare sector, the need for simultaneous, multi-annotated database of medical images for evaluations of novel DL systems grows. This study looked at DL algorithms that distinguish retinal images by the side of the eyes (Left and Right side) as well as the field positioning (Macular-centred or Optic Disc-centred) and evaluated these algorithms against a large dataset comprised of 7,953 images from multi-ethnic populations. For these convolutional neural networks, L/R model and Mac/OD model, a high AUC (0.978, 0.990), sensitivity (95.9%, 97.6%), specificity (95.5%, 96.7%) and accuracy (95.7%, 97.2%) were found, respectively, for the primary validation sets. The models presented high performance also using the external validation database.
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References
Burlina, P.M., Joshi, N., Pekala, M., Pacheco, K.D., Freund, D.E., Bressler, N.M.: Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 135(11), 1170–1176 (2017)
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)
Grassmann, F., 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. Ophthalmology (2018)
Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)
Ho, H., et al.: Association of systemic medication use with intraocular pressure in a multiethnic Asian population: the Singapore epidemiology of eye diseases study. JAMA Ophthalmol. 135(3), 196–202 (2017)
Lakhani, P., Sundaram, B.: Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2), 574–582 (2017)
Li, Z., He, Y., Keel, S., Meng, W., Chang, R.T., He, M.: Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology (2018)
Ting, D.S.W., 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 318(22), 2211–2223 (2017)
Wong, T.Y., Bressler, N.M.: Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. JAMA 316(22), 2366–2367 (2016)
Xu, Y., et al.: Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinf. 18(1), 281 (2017)
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Bellemo, V. et al. (2019). Artificial Intelligence Using Deep Learning in Classifying Side of the Eyes and Width of Field for Retinal Fundus Photographs. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_26
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DOI: https://doi.org/10.1007/978-3-030-21074-8_26
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