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Clinically applicable deep learning for diagnosis and referral in retinal disease

De Fauw, J; Ledsam, JR; Romera-Paredes, B; Nikolov, S; Tomasev, N; Blackwell, S; Askham, H; ... Ronneberger, O; + view all (2018) Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine , 24 (9) pp. 1342-1350. 10.1038/s41591-018-0107-6.

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Abstract

The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. 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. 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. 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. 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. 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. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting.

Type: Article
Title: Clinically applicable deep learning for diagnosis and referral in retinal disease
Location: United States
DOI: 10.1038/s41591-018-0107-6
Publisher version: http://dx.doi.org/10.1038/s41591-018-0107-6
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
URI: http://discovery.ucl.ac.uk/id/eprint/10056194
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