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Artificial intelligence for diagnosis of inherited retinal disease: an exciting opportunity and one step forward
  1. Tien-En Tan1,2,3,
  2. Hwei Wuen Chan4,5,
  3. Mandeep Singh6,
  4. Tien Yin Wong1,2,3,
  5. Jose S Pulido5,
  6. Michel Michaelides5,7,
  7. Elliott H Sohn8,
  8. Daniel Ting1,2,3
  1. 1Singapore National Eye Centre, Singapore
  2. 2Singapore Eye Research Institute, Singapore
  3. 3Ophthalmology & Visual Sciences Academic Clinical Programme (EYE ACP), Duke-NUS Medical School, Singapore
  4. 4Department of Ophthalmology, National University of Singapore, Singapore
  5. 5UCL Institute of Ophthalmology, University College London, London, UK
  6. 6Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland, USA
  7. 7Moorfields Eye Hospital NHS Foundation Trust, London, UK
  8. 8Department of Ophthalmology, University of Iowa, Iowa City, Iowa, USA
  1. Correspondence to Dr Daniel Ting, Singapore National Eye Centre, Singapore 168751, Singapore; daniel.ting45{at}gmail.com

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Introduction

Inherited retinal disease (IRD) affects approximately 1 in 3000 individuals in North America and Europe, and is a significant cause of visual impairment and blindness among children and working-age adults, with major personal and societal impact.1 2 Accurate clinical phenotypic and genotypic diagnosis of IRD is challenging, but increasingly important and relevant. Traditionally, genotypic diagnosis has been considered ‘nice to have’, but not ‘essential’, with implications usually related to patient prognostication and genetic counselling. However, an accurate genetic diagnosis is now of paramount importance because of rapid advances in potential gene replacement and other therapies for these previously untreatable conditions. In 2017, the first gene therapy for IRD was approved by the US Food and Drug Administration for the treatment of RPE65-mediated retinal dystrophy, and shortly after by the European Medicines Agency as well.3 Multiple clinical trials are currently underway for other IRDs, including choroideraemia, Stargardt disease and retinitis pigmentosa (RP).4 5 Besides gene replacement therapy, progress in other areas such as antisense oligonucleotide therapy and gene editing with clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins also rely on accurate genetic diagnosis.5 6

Unmet clinical challenges

Successful genotypic diagnosis remains elusive for many patients globally, due in part to remaining gaps in knowledge, but also due to limited access to testing, which remains relatively expensive, along with scarcity and an uneven distribution of institutions with expertise in IRD. In certain tertiary centres in the western world, patients have a high chance of an accurate genetic diagnosis. Recent studies have demonstrated the successful characterisation of large cohorts of patients with IRD using systematic clinical phenotyping and genetic testing protocols.7–10 Typically, historical, clinical, electrophysiological and multi-modal imaging data are used to assign each patient a clinical phenotypic category and to facilitate the selection of a genetic …

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Footnotes

  • Contributors T-ET, HWC, MS, TYW, JSP, MM, EHS and DT contributed to drafting and revising the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests TYW and DT are coinventors, with patents pending, for a deep learning system for diabetic retinopathy, glaucoma and age-related macular degeneration (SG Non-Provisional Application number 10201706186V), and a computer-implemented method for training an image classifier using weakly annotated training data (SG Provisional Patent Application number 10201901083Y) and are co-founders and shareholders of EyRIS, Singapore.

  • Provenance and peer review Commissioned; internally peer reviewed.

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