ArticleThe British journal of ophthalmology2021
Prediction of causative genes in inherited retinal disorder from fundus photography and autofluorescence imaging using deep learning techniques.
Article in The British journal of ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 43 citations in OpenAlex.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- Diagnostic Accuracy of AI Models in Detecting Different Inherited Retinal Diseases: A Systematic Review and Meta-Analysis.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
- Genetic Testing in Inherited Retinal Disease: Current Strategies and Future Directions.Journal of personalized medicine · 2026Review
- Clinical and Genetic Characteristics of a Chinese Occult Maculopathy Cohort.Translational vision science & technology · 2026Article
- 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
- Article
- A self-supervised learning method for detection of retinitis pigmentosa and Stargardt disease.Scientific reports · 2025Article
- The Role of Artificial Intelligence in Imaging-Based Diagnosis of Retinal Dystrophy and Evaluation of Gene Therapy Efficacy.Journal of personalized medicine · 2025Review
- Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.American journal of medical genetics. Part A · 2025Review
- Retinitis Pigmentosa: From Genetic Insights to Innovative Therapeutic Approaches-A Literature Review.Medicina (Kaunas, Lithuania) · 2025Review
- Reliability of clinical impressions and optimal genetic diagnostic strategies of heritable connective tissue disorders with ocular involvement in a large Chinese cohort.Human genomics · 2025Article
- Classification of fundus autofluorescence images based on macular function in retinitis pigmentosa using convolutional neural networks.Japanese journal of ophthalmology · 2025Observational
- Phenotypic Distinctions Between EYS- and USH2A-Associated Retinitis Pigmentosa in an Asian Population.Translational vision science & technology · 2025Article
- Next-generation phenotyping of inherited retinal diseases from multimodal imaging with Eye2Gene.Nature machine intelligence · 2025Article
- Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: a review.Eye and vision (London, England) · 2024Review
- Article
- Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites.Nature communications · 2024Article
- 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
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
aimsTo investigate the utility of a data-driven deep learning approach in patients with inherited retinal disorder (IRD) and to predict the causative genes based on fundus photography and fundus autofluorescence (FAF) imaging.
methodsClinical and genetic data from 1302 subjects from 729 genetically confirmed families with IRD registered with the Japan Eye Genetics Consortium were reviewed. Three categories of genetic diagnosis were selected, based on the high prevalence of their causative genes: Stargardt disease (
resultsA total of 417 images from 156 Japanese subjects were examined, including 115 genetically confirmed patients caused by the three prevalent causative genes and 41 normal subjects. The mean overall test accuracy for fundus photographs and FAF images was 88.2% and 81.3%, respectively. The mean overall sensitivity/specificity values for fundus photographs and FAF images were 88.3%/97.4% and 81.8%/95.5%, respectively.
conclusionA novel application of deep neural networks in the prediction of the causative IRD genes from fundus photographs and FAF, with a high prediction accuracy of over 80%, was highlighted. These achievements will extensively promote the quality of medical care by facilitating early diagnosis, especially by non-specialists, access to care, reducing the cost of referrals, and preventing unnecessary clinical and genetic testing.
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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.