ArticleNature machine intelligence2025
Next-generation phenotyping of inherited retinal diseases from multimodal imaging with Eye2Gene.
Article in Nature machine intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial.Nature medicine · 2026Trial
- Whole-Exome Sequencing in a Consanguinity-Enriched South Indian Retinitis Pigmentosa Cohort: Diagnostic Yield and Molecular Spectrum.Ophthalmology science · 2026Article
- Deep Learning for Diagnosis of Choroideremia andOphthalmology science · 2026Article
- A text mining and ontology-based approach using phenotypes to obtain relevant literature for rare diseases.iScience · 2026Article
- Article
- Genetic Testing in Inherited Retinal Disease: Current Strategies and Future Directions.Journal of personalized medicine · 2026Review
- Interactive multimodal disease risk evaluation using a self-service non-invasive device.Communications medicine · 2026Article
- Deep learning detection of retinitis pigmentosa inheritance forms through synthetic data expansion of a rare disease dataset.Scientific reports · 2026Article
- Visual Field Estimation in X-Linked Retinitis Pigmentosa Associated with Retinitis Pigmentosa GTPase Regulator (Ophthalmology science · 2026Article
- Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.Ophthalmology and therapy · 2026Review
- Leveraging large scale deep learning models for diagnosis and visual outcome prediction in retinitis pigmentosa.NPJ digital medicine · 2026Article
- Artificial intelligence in ophthalmology clinical trials: a narrative review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Article
- The expanding role of artificial intelligence in personalised medicine: from innovation to individualized care.Frontiers in medicine · 2026Review
- 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
- Enhanced CoAtNet based hybrid deep learning architecture for automated tuberculosis detection in human chest X-rays.BMC medical imaging · 2025Article
- Stargardt's Connected Research Network Inaugural Meeting: Landscape Review and Horizon Scanning of Stargardt Disease.Translational vision science & technology · 2025Review
- Characterizing the non-pigmented retinitis pigmentosa in a Chinese cohort: Insights into phenotype, genotype, and disease progression.Advances in ophthalmology practice and researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
47 authors.
Funding
Abstract
Rare eye diseases such as inherited retinal diseases (IRDs) are challenging to diagnose genetically. IRDs are typically monogenic disorders and represent a leading cause of blindness in children and working-age adults worldwide. A growing number are now being targeted in clinical trials, with approved treatments increasingly available. However, access requires a genetic diagnosis to be established sufficiently early. Critically, the timely identification of a genetic cause remains challenging. We demonstrate that a deep learning algorithm, Eye2Gene, trained on a large multimodal imaging dataset of individuals with IRDs (
Indexed as
Identifiers
What OpenQuestion holds
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.