ReviewGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2024
Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence.
Review in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
What it found
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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.
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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Retinal biomarkers for early Alzheimer's detection: a systematic review of optical coherence tomography (OCT) findings.BMJ open ophthalmology · 2026Pooled it
- Quantitative ophthalmic posterior segment optical coherence tomography angiography and neurologic conditions: a review.Frontiers in neurologyPooled it
- Exploring Alzheimer Disease from a Retinal and Ocular Perspective.Biomedicines · 2026Review
- Fusion-ADiNet: a multi-level framework for enhanced diabetes and Alzheimer's disease detection using chimp-whale fusion estimation.Scientific reports · 2025Article
- Eye and Systemic Disease Management Changes After Teleophthalmology Screening in Primary Care: Retrospective Cross-Sectional Pilot Study of 200 Consecutive Patients.JMIR formative research · 2025Article
- An explainable transformer model for Alzheimer's disease detection using retinal imaging.Scientific reports · 2025Article
- High-resolution fundus images for ophthalmomics and early cardiovascular disease prediction.Scientific data · 2025Article
- Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Retinal capillary density among healthy Egyptian and South Asian students: an optical coherence tomography angiography study.International journal of ophthalmology · 2025Article
- Artificial intelligence in stroke risk assessment and management via retinal imaging.Frontiers in computational neuroscience · 2025Review
- Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition.Cancers · 2024Review
- Artificial Intelligence in Eye Movements Analysis for Alzheimer's Disease Early Diagnosis.Current Alzheimer research · 2024Review
- Synthetic data analysis for early detection of Alzheimer progression through machine learning algorithms.PeerJ. Computer science · 2024Article
- Utilizing deep learning to predict Alzheimer's disease and mild cognitive impairment with optical coherence tomography.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a neurodegenerative condition that primarily affects brain tissue. Because the retina and brain share the same embryonic origin, visual deficits have been reported in AD patients. Artificial Intelligence (AI) has recently received a lot of attention due to its immense power to process and detect image hallmarks and make clinical decisions (like diagnosis) based on images. Since retinal changes have been reported in AD patients, AI is being proposed to process images to predict, diagnose, and prognosis AD. As a result, the purpose of this review was to discuss the use of AI trained on retinal images of AD patients. According to previous research, AD patients experience retinal thickness and retinal vessel density changes, which can occasionally occur before the onset of the disease's clinical symptoms. AI and machine vision can detect and use these changes in the domains of disease prediction, diagnosis, and prognosis. As a result, not only have unique algorithms been developed for this condition, but also databases such as the Retinal OCTA Segmentation dataset (ROSE) have been constructed for this purpose. The achievement of high accuracy, sensitivity, and specificity in the classification of retinal images between AD and healthy groups is one of the major breakthroughs in using AI based on retinal images for AD. It is fascinating that researchers could pinpoint individuals with a positive family history of AD based on the properties of their eyes. In conclusion, the growing application of AI in medicine promises its future position in processing different aspects of patients with AD, but we need cohort studies to determine whether it can help to follow up with healthy persons at risk of AD for a quicker diagnosis or assess the prognosis of patients with AD.
Indexed as
Identifiers
38358524What 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.