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

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.

[1]  Daniele Merico,et al.  Brain-expressed exons under purifying selection are enriched for de novo mutations in autism spectrum disorder , 2014, Nature Genetics.

[2]  P. Scanlon,et al.  Article Commentary: The English national screening programme for sight-threatening diabetic retinopathy , 2008, Journal of medical screening.

[3]  Geoffrey E. Hinton,et al.  Learning to Detect Roads in High-Resolution Aerial Images , 2010, ECCV.

[4]  M. Ulbig,et al.  Diabetic retinopathy: Early diagnosis and effective treatment. , 2010, Deutsches Arzteblatt international.

[5]  N. Bressler Age-related macular degeneration is the leading cause of blindness... , 2004, JAMA.

[6]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[7]  J. Fujimoto,et al.  Optical Coherence Tomography , 1991 .

[8]  C. Bunce,et al.  Causes of blind and partial sight certifications in England and Wales: April 2007–March 2008 , 2010, Eye.

[9]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[10]  David A. Clifton,et al.  Gaussian Processes for Personalized e-Health Monitoring With Wearable Sensors , 2013, IEEE Transactions on Biomedical Engineering.

[11]  J. Shaw,et al.  Global estimates of the prevalence of diabetes for 2010 and 2030. , 2010, Diabetes research and clinical practice.

[12]  Richard Wormald,et al.  The estimated prevalence and incidence of late stage age related macular degeneration in the UK , 2012, British Journal of Ophthalmology.