- Jeffrey De Fauw
- Joe Ledsam
- 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
- Balaji Lakshminarayanan
- Clemens Meyer
- Faith Mackinder
- Simon Bouton
- Kareem Ayoub
- Reena Chopra
- Dominic King
- Alan Karthikesalingam
- 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
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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