Research examples
Here we list a selection of academic papers and research projects where advanced analytics, including machine learning and artificial intelligence, have been applied to ophthalmic datasets for patient benefit. Many of these examples involve members of the INSIGHT team or our partners. All links are to external sites.
A list of papers related specifically to INSIGHT can be found on the HDR Innovation Gateway
Artificial Intelligence
Recent regulatory reports regarding Artificial Intelligence as a Medical Device:
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The Regulation of Artificial Intelligence as a Medical Device (publishing.service.gov.uk) This report was led by Alastair Denniston with support from Parag Vyas on behalf of the RHC142 and supported by a team of Civil Servants within BEIS. Key contributors from the BEIS team were Katie Francis, Cara Nicol and Tanuj Jain. (Report published November 2022)
Retinal disease
Some recent research using Artificial Intelligence and machine learning:
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Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study (Gongyu Zhang, Dun Jack Fu, Bart Liefers, Livia Faes, Sophie Glinton, Siegfried Wagner et al. Paper published in The Lancet Digital Health, September 2021)
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Predicting wet age-related macular degeneration (AMD) using DARC (detecting apoptosing retinal cells) AI (artificial intelligence) technology (paper published December 2020)
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OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications (paper published January 2018)
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Prediction of Causative Genes in Inherited Retinal Disorders from Spectral-Domain Optical Coherence Tomography Utilizing Deep Learning Techniques (paper published April 2019)
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Fully automated detection of retinal disorders by image-based deep learning (paper published January 2019)
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Eye2Gene - a web app to assist genetic diagnosis of inherited retinal disease with artificial intelligence
(project led by Nikolas Pontikos of UCL Institute of Ophthalmology and Moorfields Eye Hospital Reading Centre)
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Using AI in new ways to investigate retinal diseases (project funded by Moorfields Eye Charity project from October 2019)
Diabetic retinopathy
Here are some recent examples of research using eye datasets to investigate the effectiveness of current treatments and the potential for new screening techniques powered by artificial intelligence:
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An open-source data set of anti-VEGF therapy in diabetic macular oedema patients over 4 years and their visual acuity outcomes (paper published June 2020)
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Health Economic and Safety Considerations for Artificial Intelligence Applications in Diabetic Retinopathy Screening (paper published April 2020)
Alzheimer’s
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Insights into Systemic Disease through Retinal Imaging-Based Oculomics (paper published April 2020)
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Artificial intelligence and machine learning for Alzheimer’s disease: let’s not forget about the retina (paper published January 2021)
Glaucoma
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A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells) (paper published May 2020)
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Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice (paper published November 2020)
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The potential application of artificial intelligence for diagnosis and management of glaucoma in adults (paper published June 2020)
Cataracts
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Artificial Intelligence for Cataract Detection and Management (paper published March 2020)
Personalised medicine
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‘Delivering personalized medicine in retinal care: from artificial intelligence algorithms to clinical application’ (paper published September 2020)
Health data poverty
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‘A Global Review of Publicly Available Datasets for Ophthalmic Imaging: Recognising Barriers to Access, Usability and Generalisability’ (paper published October 2020)
Covid-19