ArticleScientific reports2018
Automatic Cone Photoreceptor Localisation in Healthy and Stargardt Afflicted Retinas Using Deep Learning.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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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
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.
- A Systematic Review of Artificial Intelligence Applications Used for Inherited Retinal Disease Management.Medicina (Kaunas, Lithuania) · 2022Pooled it
- Adaptive Optics Image Analysis Using Generative AI (GPT-4) Scripting: Exploratory Study.JMIR formative research · 2026Article
- Open-Source Software Suite for Cell Detection and Segmentation in Adaptive Optics Retinal Imaging.Translational vision science & technology · 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
- A self-supervised learning method for detection of retinitis pigmentosa and Stargardt disease.Scientific reports · 2025Article
- ISOSNet: a unified framework for cone photoreceptor detection and inner segment and outer segment length measurement from AO-OCT B-scans.Biomedical optics express · 2025Article
- Neural network assisted annotation and analysis tool to study in-vivo foveolar cone photoreceptor topography.Scientific reports · 2025Article
- Improving cone identification using merged non-confocal quadrant-detection adaptive optics scanning light ophthalmoscope images.Biomedical optics express · 2024Article
- Adaptive optics scanning laser ophthalmoscopy in a heterogenous cohort with Stargardt disease.Scientific reports · 2024Observational
- Update on Clinical Trial Endpoints in Gene Therapy Trials for Inherited Retinal Diseases.Journal of clinical medicine · 2024Review
- Reducing manual labeling requirements and improved retinal ganglion cell identification in 3D AO-OCT volumes using semi-supervised learning.Biomedical optics express · 2024Article
- Minimum intensity projection of embossed quadrant-detection images for improved photoreceptor mosaic visualisation.Frontiers in ophthalmology · 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
- Deep Density Estimation for Cone Counting and Diagnosis of Genetic Eye Diseases From Adaptive Optics Scanning Light Ophthalmoscope Images.Translational vision science & technology · 2023Article
- Characteristics of Rare Inherited Retinal Dystrophies in Adaptive Optics-A Study on 53 Eyes.Diagnostics (Basel, Switzerland) · 2023Article
- Adaptive Optics Imaging of Inherited Retinal Disease.Cold Spring Harbor perspectives in medicine · 2023Review
- Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes.Biomedical optics express · 2023Article
- Using deep learning for the automated identification of cone and rod photoreceptors from adaptive optics imaging of the human retina.Biomedical optics express · 2022Article
- Deep Learning Approaches for Automatic Localization in Medical Images.Computational intelligence and neuroscience · 2022Review
- Cone photoreceptor reflectance variation in the northern tree shrew and thirteen-lined ground squirrel.Experimental biology and medicine (Maywood, N.J.) · 2021Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
Abstract
We present a robust deep learning framework for the automatic localisation of cone photoreceptor cells in Adaptive Optics Scanning Light Ophthalmoscope (AOSLO) split-detection images. Monitoring cone photoreceptors with AOSLO imaging grants an excellent view into retinal structure and health, provides new perspectives into well known pathologies, and allows clinicians to monitor the effectiveness of experimental treatments. The MultiDimensional Recurrent Neural Network (MDRNN) approach developed in this paper is the first method capable of reliably and automatically identifying cones in both healthy retinas and retinas afflicted with Stargardt disease. Therefore, it represents a leap forward in the computational image processing of AOSLO images, and can provide clinical support in on-going longitudinal studies of disease progression and therapy. We validate our method using images from healthy subjects and subjects with the inherited retinal pathology Stargardt disease, which significantly alters image quality and cone density. We conduct a thorough comparison of our method with current state-of-the-art methods, and demonstrate that the proposed approach is both more accurate and appreciably faster in localizing cones. As further validation to the method's robustness, we demonstrate it can be successfully applied to images of retinas with pathologies not present in the training data: achromatopsia, and retinitis pigmentosa.
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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.