Automated analysis of retinal imaging using machine learning techniques for computer vision 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. https://doi.org/10.12688/f1000research.8996.2 Published in: F1000Research Document Version: Publisher's PDF, also known as Version of record Queen's University Belfast - Research Portal: Link to publication record in Queen's University Belfast Research Portal Publisher rights Copyright 2017 the authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited. 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Sep. 2021 F1000Research 2017, 5:1573 Last updated: 22 JUN 2017 STUDY PROTOCOL Automated analysis of retinal imaging using machine learning techniques for computer vision [version 2; referees: 2 approved] Jeffrey De Fauw1, Pearse Keane1,2, Nenad Tomasev1, Daniel Visentin1, George van den Driessche1, Mike Johnson1, Cian O Hughes1, Carlton Chu1, Joseph Ledsam1, Trevor Back1, Tunde Peto2, Geraint Rees3, Hugh Montgomery5, Rosalind Raine4, Olaf Ronneberger1, Julien Cornebise1 1DeepMind, London, EC4A 3TW, UK 2Moorfields Eye Hospital NHS Foundation Trust, London, EC1V 2PD, UK 3Alexandra House University College London, Bloomsbury Campus, London, WC1N 3AR, UK 4Department of Applied Heath Research, University College London, London, WC1E 7HB, UK 5Institute of Sport, Exercise and Health, London, W1T 7HA, UK v2 First published: 05 Jul 2016, 5:1573 (doi: 10.12688/f1000research.8996.1) Open Peer Review Latest published: 22 Jun 2017, 5:1573 (doi: 10.12688/f1000research.8996.2) Referee Status: 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 Invited Referees sighted. Sight threatening diseases, such as diabetic retinopathy and age 1 2 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 version 2 prevented in many cases. published 22 Jun 2017 Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet”) version 1 age-related macular degeneration (wet AMD) and diabetic retinopathy. Two published report report 05 Jul 2016 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). 1 Yit C. Yang , Royal Wolverhampton NHS Changes in population demographics and expectations and the changing Trust, UK pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone 2 Sandrine Zweifel , University Hospital to human error. The application of novel analysis methods may provide a Zürich, Switzerland solution to these challenges. This research will focus on applying novel machine learning algorithms to Discuss this article automatic analysis of both digital fundus photographs and OCT in Moorfields Comments (0) 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 Page 1 of 8 F1000Research 2017, 5:1573 Last updated: 22 JUN 2017 provide novel quantitative measures for specific disease features and for monitoring the therapeutic success. Corresponding author: Joseph Ledsam ([email protected]) Competing interests: Moorfields Eye Hospital NHS Foundation Trust administration time spent on this work will be paid to the trust. The Chief Investigator and some co-investigators are paid employees of DeepMind. Several co-investigators (PK, GR, RR) are paid contractors for DeepMind. How to cite this article: De Fauw J, Keane P, Tomasev N et al. Automated analysis of retinal imaging using machine learning techniques for computer vision [version 2; referees: 2 approved] F1000Research 2017, 5:1573 (doi: 10.12688/f1000research.8996.2) Copyright: © 2017 De Fauw J et al. This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Grant information: DeepMind is funding the research. First published: 05 Jul 2016, 5:1573 (doi: 10.12688/f1000research.8996.1) Page 2 of 8 F1000Research 2017, 5:1573 Last updated: 22 JUN 2017 Recent healthcare applications of such algorithms have shed light REVISED Amendments from Version 1 on complex genetic interactions in autism (Uddin et al., 2014) and Minor changes incorporating reviewer comments and updates to monitoring of physiological observations in intensive care (Clifton the approvals section to incorporate the HRA approval. et al., 2013). See referee reports This study aims to combine traditional statistical methodology and machine learning algorithms to achieve automatic grading and quantitative analysis of both digital fundus photograph and Background OCT in Moorfields Eye Hospital NHS Foundation Trust patients Age-related macular degeneration (AMD) is a degenerative retinal (London, UK). Moorfields Eye Hospital is the leading provider of disease that can cause irreversible visual loss (Bressler, 2004). It is eye health services in the UK and a world-class centre of excellence the leading cause of blindness in Europe and North America and for ophthalmic research and education. Should the research be suc- accounts for over half of partially sighted or legally blind certifica- cessful, implementation of the outcomes would improve patient tions in the UK (Bunce et al., 2010). Neovascular (“wet”) AMD access to treatment and ease pressures on time and resources in is an advanced form of macular degeneration that historically has ophthalmology clinics. accounted for the majority of vision loss related to AMD. It is characterised by abnormal blood vessel growth that can result in Aims and objectives hemorrhage, fluid exudation and fibrosis, and thus to local macular Primary objective damage and ultimately vision loss (Owen et al., 2012). 1.1 Exploratory study: investigate whether computer algorithms can Diabetic retinopathy (DR) is the leading cause of blindness in detect and classify pathological features on eye imaging, including working age populations in the developed world (Cheung et al., fundus digital photographs and OCT. 2010). It is estimated that up to 50% of people with proliferative DR (characterised by neovascularisation) who do not receive Secondary objectives timely treatment will become legally blind within 5 years (Shaw If the exploratory study is successful: et al., 2010). Although up to 98% of severe visual loss due to DR can be prevented with early detection and treatment, once it has 2.1 progressed vision loss is often permanent (Kollias & Ulbig, 2010). To provide novel image analysis algorithms to identify and quantify Indeed, 4,200 people in England every year are at risk of blindness specific pathological features in eye imaging, using validated meth- caused by diabetic retinopathy and there are 1,280 new cases of ods and expert clinical consensus. blindness caused by diabetic retinopathy (Scanlon, 2008). 2.2 To diagnose these conditions and monitor their progression (and To provide quantitative measurements disease progression, severity response to treatment) the presence and precise location of the and to monitor the therapeutic success over time. lesions must be determined. Two imaging modalities are com- monly used for this purpose: digital photographs of the fundus (the Study design ‘back’ of the eye) and Optical Coherence Tomography (OCT, a This is a retrospective, non-interventional exploratory study. modality that uses light waves in a similar way to how ultrasound Analyses performed in the study will be on fully anonymised (to uses sound waves) (Huang et al., 1991). achieve the primary objective) and pseudonymised (to achieve the secondary objective 2.2) retinal images (including fundus images Clinics such as these have generated very large datasets of both and OCT). These