Google Research

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

  • 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
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.

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