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.
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