ArticleBMJ open2023
Can artificial intelligence accelerate the diagnosis of inherited retinal diseases? Protocol for a data-only retrospective cohort study (Eye2Gene).
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Current Applications of Artificial Intelligence for Fuchs Endothelial Corneal Dystrophy: A Systematic Review.Translational vision science & technology · 2025Pooled it
- AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial.Nature medicine · 2026Trial
- Artificial Intelligence in Clinical Genetics: Current Applications and Challenges.Indian journal of pediatrics · 2026Review
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- Artificial intelligence applications in inherited retinal dystrophies.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Leveraging large scale deep learning models for diagnosis and visual outcome prediction in retinitis pigmentosa.NPJ digital medicine · 2026Article
- Using deep learning to identify inherited retinal diseases based on wide-field retinal imaging data.PloS one · 2026Article
- The Role of Artificial Intelligence in Imaging-Based Diagnosis of Retinal Dystrophy and Evaluation of Gene Therapy Efficacy.Journal of personalized medicine · 2025Review
- AI-Assisted Optical Coherence Tomography Segmentation for Enhanced Diagnosis of Inherited Retinal Diseases.Translational vision science & technology · 2025Article
- Inherited retinal disease pathway in the UK: a patient perspective and the potential of AI.The British journal of ophthalmology · 2025Article
- Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.American journal of medical genetics. Part A · 2025Review
- Stargardt's Connected Research Network Inaugural Meeting: Landscape Review and Horizon Scanning of Stargardt Disease.Translational vision science & technology · 2025Review
- Quantification of Optical Coherence Tomography Features in >3500 Patients with Inherited Retinal Disease Reveals Novel Genotype-Phenotype Associations.medRxiv : the preprint server for health sciences · 2025Article
- Surface Electromyography Combined with Artificial Intelligence in Predicting Neuromuscular Falls in the Elderly: A Narrative Review of Present Applications and Future Perspectives.Healthcare (Basel, Switzerland) · 2025Review
- Artificial intelligence in clinical genetics.European journal of human genetics : EJHG · 2025Review
- Behind the mask: a critical perspective on the ethical, moral, and legal implications of AI in ophthalmology.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Review
- Distinct Clinical Effects of Two RP1L1 Hotspots in East Asian Patients With Occult Macular Dystrophy (Miyake Disease): EAOMD Report 4.Investigative ophthalmology & visual science · 2024Article
- Multimodal imaging and deep learning in geographic atrophy secondary to age-related macular degeneration.Acta ophthalmologica · 2023Review
- Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of >3500 Patients with Inherited Retinal Disease from the United Kingdom.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
28 authors at 8 institutions in 5 countries.
Funding
Abstract
introductionInherited retinal diseases (IRD) are a leading cause of visual impairment and blindness in the working age population. Mutations in over 300 genes have been found to be associated with IRDs and identifying the affected gene in patients by molecular genetic testing is the first step towards effective care and patient management. However, genetic diagnosis is currently slow, expensive and not widely accessible. The aim of the current project is to address the evidence gap in IRD diagnosis with an AI algorithm, Eye2Gene, to accelerate and democratise the IRD diagnosis service. METHODS AND ANALYSIS: The data-only retrospective cohort study involves a target sample size of 10 000 participants, which has been derived based on the number of participants with IRD at three leading UK eye hospitals: Moorfields Eye Hospital (MEH), Oxford University Hospital (OUH) and Liverpool University Hospital (LUH), as well as a Japanese hospital, the Tokyo Medical Centre (TMC). Eye2Gene aims to predict causative genes from retinal images of patients with a diagnosis of IRD. For this purpose, 36 most common causative IRD genes have been selected to develop a training dataset for the software to have enough examples for training and validation for detection of each gene. The Eye2Gene algorithm is composed of multiple deep convolutional neural networks, which will be trained on MEH IRD datasets, and externally validated on OUH, LUH and TMC. ETHICS AND DISSEMINATION: This research was approved by the IRB and the UK Health Research Authority (Research Ethics Committee reference 22/WA/0049) 'Eye2Gene: accelerating the diagnosis of IRDs' Integrated Research Application System (IRAS) project ID: 242050. All research adhered to the tenets of the Declaration of Helsinki. Findings will be reported in an open-access format.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.