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

Jeffrey De Fauw, Pearse Keane, Nenad Tomasev, Daniel Visentin, George van den Driessche, Mike Johnson, Cian O Hughes, Carlton Chu, Joseph Ledsam, Trevor Back, Tunde Peto, Geraint Rees, Hugh Montgomery, Rosalind Raine, Olaf Ronneberger, Julien Cornebise

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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.

Original languageEnglish
Pages (from-to)1573
Early online date05 Jul 2016
Publication statusPublished - 22 Jun 2017
Externally publishedYes


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    De Fauw, J., Keane, P., Tomasev, N., Visentin, D., van den Driessche, G., Johnson, M., Hughes, C. O., Chu, C., Ledsam, J., Back, T., Peto, T., Rees, G., Montgomery, H., Raine, R., Ronneberger, O., & Cornebise, J. (2017). Automated analysis of retinal imaging using machine learning techniques for computer vision. F1000Research, 5, 1573.