Automated analysis of retinal imaging using machine learning techniques for computer vision.

Journal: F1000Research
Published Date: Jul 5, 2016

Abstract

There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases. Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular ("wet") age-related macular degeneration (wet AMD) and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the 'back' of the eye) and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves). Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges. This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients. Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

Authors

  • Jeffrey De Fauw
    DeepMind, London, EC4A 3TW, UK.
  • Pearse Keane
    DeepMind, London, EC4A 3TW, UK.
  • Nenad Tomasev
    DeepMind, London, EC4A 3TW, UK.
  • Daniel Visentin
    DeepMind, London, EC4A 3TW, UK.
  • George van den Driessche
    DeepMind, London, EC4A 3TW, UK.
  • Mike Johnson
    DeepMind, London, EC4A 3TW, UK.
  • Cian O Hughes
    DeepMind, London, EC4A 3TW, UK.
  • Carlton Chu
    DeepMind, London, EC4A 3TW, UK.
  • Joseph Ledsam
    DeepMind, London, EC4A 3TW, UK.
  • Trevor Back
    DeepMind, London, EC4A 3TW, UK.
  • Tunde Peto
    Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom.
  • Geraint Rees
    Institute of Neurology, University College London, London, WC1N 3BG, UK.
  • Hugh Montgomery
    Institute of Sport, Exercise and Health, London, W1T 7HA, UK.
  • Rosalind Raine
    Department of Applied Heath Research, University College London, London, WC1E 7HB, UK.
  • Olaf Ronneberger
    DeepMind, London, EC4A 3TW, UK.
  • Julien Cornebise
    DeepMind, London, EC4A 3TW, UK.

Keywords

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