ArticleThe British journal of ophthalmology2023
Prediction of visual impairment in retinitis pigmentosa using deep learning and multimodal fundus images.
Article in The British journal of ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 33 citations in OpenAlex.
- Deep Learning for Diagnosis of Choroideremia andOphthalmology science · 2026Article
- Early dynamic response on spectral-domain OCT predicts long-term visual outcome in diabetic macular edema treated with aflibercept.BMC ophthalmology · 2026Article
- 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
- AI-based prediction of best-corrected visual acuity in patients with multiple retinal diseases using multimodal medical imaging.The British journal of ophthalmology · 2026Article
- Leveraging large scale deep learning models for diagnosis and visual outcome prediction in retinitis pigmentosa.NPJ digital medicine · 2026Article
- Article
- Detection of cystoid macular edema in patients with retinitis pigmentosa based on deep learning.International journal of retina and vitreous · 2025Article
- Early detection and staging of retinitis pigmentosa using multifocal electroretinogram parameters and machine learning algorithms.Physical and engineering sciences in medicine · 2025Article
- Classification of fundus autofluorescence images based on macular function in retinitis pigmentosa using convolutional neural networks.Japanese journal of ophthalmology · 2025Observational
- When do patients with retinitis pigmentosa present to ophthalmologists? A multi-centre retrospective study.Eye (London, England) · 2024Article
- Determinants of Visual Impairment Among Chinese Middle-Aged and Older Adults: Risk Prediction Model Using Machine Learning Algorithms.JMIR aging · 2024Article
- Multimodality Fusion Strategies in Eye Disease Diagnosis.Journal of imaging informatics in medicine · 2024Article
- Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.Medicina (Kaunas, Lithuania) · 2024Review
- Cataract surgery in retinitis pigmentosa.Medical hypothesis, discovery & innovation ophthalmology journal · 2024Review
- Artificial intelligence in retinal disease: clinical application, challenges, and future directions.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Review
- Estimation of Visual Function Using Deep Learning From Ultra-Widefield Fundus Images of Eyes With Retinitis Pigmentosa.JAMA ophthalmology · 2023Article
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundThe efficiency of clinical trials for retinitis pigmentosa (RP) treatment is limited by the screening burden and lack of reliable surrogate markers for functional end points. Automated methods to determine visual acuity (VA) may help address these challenges. We aimed to determine if VA could be estimated using confocal scanning laser ophthalmoscopy (cSLO) imaging and deep learning (DL).
methodsSnellen corrected VA and cSLO imaging were obtained retrospectively. The Johns Hopkins University (JHU) dataset was used for 10-fold cross-validations and internal testing. The Amsterdam University Medical Centers (AUMC) dataset was used for external independent testing. Both datasets had the same exclusion criteria: visually significant media opacities and images not centred on the central macula. The JHU dataset included patients with RP with and without molecular confirmation. The AUMC dataset only included molecularly confirmed patients with RP. Using transfer learning, three versions of the ResNet-152 neural network were trained: infrared (IR), optical coherence tomography (OCT) and combined image (CI).
resultsIn internal testing (JHU dataset, 2569 images, 462 eyes, 231 patients), the area under the curve (AUC) for the binary classification task of distinguishing between Snellen VA 20/40 or better and worse than Snellen VA 20/40 was 0.83, 0.87 and 0.85 for IR, OCT and CI, respectively. In external testing (AUMC dataset, 349 images, 166 eyes, 83 patients), the AUC was 0.78, 0.87 and 0.85 for IR, OCT and CI, respectively.
conclusionsOur algorithm showed robust performance in predicting visual impairment in patients with RP, thus providing proof-of-concept for predicting structure-function correlation based solely on cSLO imaging in patients with RP.
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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.