![]() Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. RetiNet: automatic AMD identification in OCT volumetric data. Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. Delay in treating age-related macular degeneration in Spain is associated with progressive vision loss. Delay between medical indication to anti-VEGF treatment in age-related macular degeneration can result in a loss of visual acuity. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. in Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. 3D U-Net: learning dense volumetric segmentation from sparse annotation. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. in Navab N., Hornegger J., Wells W., Frangi A. U-Net: convolutional networks for biomedical image segmentation. A modeled economic analysis of a digital teleophthalmology system as used by three federal healthcare agencies for detecting proliferative diabetic retinopathy. ![]() How to defuse a demographic time bomb: the way forward? Eye 31, 1519–1522 (2017). Dermatologist-level classification of skin cancer with deep neural networks. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. A view of the current and future role of optical coherence tomography in the management of age-related macular degeneration. Schmidt-Erfurth, U., Klimscha, S., Waldstein, S. Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis. Incidence of late-stage age-related macular degeneration in American whites: systematic review and meta-analysis. The estimated prevalence and incidence of late stage age related macular degeneration in the UK. Surveillance of sight loss due to delay in ophthalmic treatment or review: frequency, cause and outcome. Magnetic resonance imaging (MRI) exams (indicator). Computed tomography (CT) exams (indicator). Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation referral accuracy is maintained when using tissue segmentations from a different type of device. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Nature Medicine volume 24, pages 1342–1350 ( 2018) Cite this article Clinically applicable deep learning for diagnosis and referral in retinal disease
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