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Clinically applicable deep learning for diagnosis and referral in retinal optical coherence tomography

Jeffrey De Fauw
Bernardino Romera Paredes
Stanislav Nikolov Nikolov
Nenad Tomašev
Sam Julian Blackwell
Harry Askham
Xavier Glorot
Brendan O'Donoghue
Daniel James Visentin
George van den Driessche
Clemens Meyer
Faith Mackinder
Simon Bouton
Kareem Ayoub
Reena Chopra
Dominic King
Cían Hughes
Rosalind Raine
Julian Hughes
Dawn Sim
Catherine Egan
Adnan Tufail
Hugh Montgomery
Demis Hassabis
Geraint Rees
Trevor John Back
Peng Khaw
Mustafa Suleyman
Julien Cornebise
Pearse Keane
Olaf Ronneberger
Nature (2018)


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.