ArticleScientific reports2020
Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Artificial Intelligence Applications Used for Inherited Retinal Disease Management.Medicina (Kaunas, Lithuania) · 2022Pooled it
- Anatomy- versus Sensitivity-Based Loci Preselection in Detecting USH2A-Retinopathy Microperimetric Progression.Ophthalmology science · 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
- Classification of Inherited Retinal Diseases Using Artificial Intelligence Models for Fundus Autofluorescence and Ultrawide Retinal Images.Journal of ophthalmology · 2026Article
- A self-supervised learning method for detection of retinitis pigmentosa and Stargardt disease.Scientific reports · 2025Article
- Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.American journal of medical genetics. Part A · 2025Review
- Article
- Advances in machine learning for ABCA4-related retinopathy: segmentation and phenotyping.International ophthalmology · 2025Review
- Progression of Atrophy as a Function of ABCA4 Variants and Age of Onset in Stargardt Disease.Investigative ophthalmology & visual science · 2025Article
- Quantifying the Progression of Stargardt Disease in Double-Null ABCA4 Carriers Using Fundus Autofluorescence Imaging.Translational vision science & technology · 2025Article
- Clinical and Genetic Characteristics of Patients with Peripheral Retinal Flecks in Koreans.Korean journal of ophthalmology : KJO · 2024Article
- Emerging Therapeutic Approaches and Genetic Insights in Stargardt Disease: A Comprehensive Review.International journal of molecular sciences · 2024Review
- Recurrent and Concurrent Prediction of Longitudinal Progression of Stargardt Atrophy and Geographic Atrophy.medRxiv : the preprint server for health sciences · 2024Article
- 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
- Using Ensemble OCT-Derived Features beyond Intensity Features for Enhanced Stargardt Atrophy Prediction with Deep Learning.Applied sciences (Basel, Switzerland) · 2023Article
- Artificial intelligence for assessment of Stargardt macular atrophy.Neural regeneration research · 2022Review
- Automated Segmentation of Autofluorescence Lesions in Stargardt Disease.Ophthalmology. Retina · 2022Article
- Automated segmentation and feature discovery of age-related macular degeneration and Stargardt disease via self-attended neural networks.Scientific reports · 2022Article
- Kinetics of Heterogeneous Background in Stargardt's Disease over Time.Life (Basel, Switzerland) · 2022Article
- Stargardt disease: Multimodal imaging: A review.Clinical & experimental ophthalmology · 2021Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stargardt disease is one of the most common forms of inherited retinal disease and leads to permanent vision loss. A diagnostic feature of the disease is retinal flecks, which appear hyperautofluorescent in fundus autofluorescence (FAF) imaging. The size and number of these flecks increase with disease progression. Manual segmentation of flecks allows monitoring of disease, but is time-consuming. Herein, we have developed and validated a deep learning approach for segmenting these Stargardt flecks (1750 training and 100 validation FAF patches from 37 eyes with Stargardt disease). Testing was done in 10 separate Stargardt FAF images and we observed a good overall agreement between manual and deep learning in both fleck count and fleck area. Longitudinal data were available in both eyes from 6 patients (average total follow-up time 4.2 years), with both manual and deep learning segmentation performed on all (n = 82) images. Both methods detected a similar upward trend in fleck number and area over time. In conclusion, we demonstrated the feasibility of utilizing deep learning to segment and quantify FAF lesions, laying the foundation for future studies using fleck parameters as a trial endpoint.
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