ArticleScientific reports2021
Modular machine learning for Alzheimer's disease classification from retinal vasculature.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning and Machine Learning Algorithms for Retinal Image Analysis in Neurodegenerative Disease: Systematic Review of Datasets and Models.Translational vision science & technology · 2024Pooled it
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Current Applications of Artificial Intelligence in Neuro-Ophthalmic Imaging: A Narrative Approach.Medical sciences (Basel, Switzerland) · 2026Review
- Exploring Alzheimer Disease from a Retinal and Ocular Perspective.Biomedicines · 2026Review
- Artificial intelligence and neurodegeneration: state of the art and explainability.International journal of retina and vitreous · 2026Review
- An Ensemble Learning Artificial Intelligence Model for Alzheimer's Disease Detection Using OCT.Ophthalmology science · 2026Article
- Non-invasive screening of alzheimer's disease via label-free tri-spectral retinal imaging.Scientific reports · 2026Article
- Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.Frontiers in medicine · 2026Review
- Review
- IoMT driven Alzheimer's prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0.Scientific reports · 2025Article
- Ocular changes as potential biomarkers for early diagnosis of Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- An explainable transformer model for Alzheimer's disease detection using retinal imaging.Scientific reports · 2025Article
- Alzheimer's Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network.Diagnostics (Basel, Switzerland) · 2025Article
- Color Fundus Photography and Deep Learning Applications in Alzheimer Disease.Mayo Clinic proceedings. Digital health · 2024Article
- Article
- Value proposition of retinal imaging in Alzheimer's disease screening: A review of eight evolving trends.Progress in retinal and eye research · 2024Review
- Early detection of dementia through retinal imaging and trustworthy AI.NPJ digital medicine · 2024Article
- Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs.Eye (London, England) · 2024Article
- Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Review
- Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging.Eye and vision (London, England) · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do not meet the specific requirements for screening procedures due to high cost and limited availability. In this work, we took the initiative to evaluate the retina, especially the retinal vasculature, as an alternative for conducting screenings for dementia patients caused by Alzheimer's disease. Highly modular machine learning techniques were employed throughout the whole pipeline. Utilizing data from the UK Biobank, the pipeline achieved an average classification accuracy of 82.44%. Besides the high classification accuracy, we also added a saliency analysis to strengthen this pipeline's interpretability. The saliency analysis indicated that within retinal images, small vessels carry more information for diagnosing Alzheimer's diseases, which aligns with related studies.
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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.