ArticleAlzheimer's & dementia (Amsterdam, Netherlands)
Utilizing deep learning to predict Alzheimer's disease and mild cognitive impairment with optical coherence tomography.
Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Multimodal Retinal Imaging for the Detection of Early Alzheimer's Disease: Combining Biochemical, Structural, and Vascular Biomarkers.Bioengineering (Basel, Switzerland) · 2026Article
- Review
- Centralized Review of Alzheimer's Disease and Related Dementias Biomedical Repositories and Computational Methods.Bioengineering (Basel, Switzerland) · 2026Review
- An Ensemble Learning Artificial Intelligence Model for Alzheimer's Disease Detection Using OCT.Ophthalmology science · 2026Article
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- Combining OCT and OCT angiography improves differentiation of mild cognitive impairment and Alzheimer's disease.Alzheimer's research & therapy · 2026Article
- Unveiling the Physiological Correlates of Cognitive Function in Healthy Adults: An In-Depth Investigation Using Optical Coherence Tomography (OCT).Journal of clinical medicine · 2026Article
- Optical coherence tomography artefact burden predicts cognitive decline and incident dementia.Frontiers in aging neuroscience · 2026Article
- PredatorNet: a clinically trustworthy deep learning framework for multi-class Alzheimer's disease classification using MRI.Frontiers in aging neuroscience · 2026Article
- AI-powered oculomics for early diagnosis of neurodegenerative diseases through retinal microvasculature analysis.Annals of medicine and surgery (2012) · 2026Article
- AlzFormer: Video-based space-time attention model for early diagnosis of Alzheimer's disease.Neuroscience · 2025Article
- AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases.International journal of emergency medicine · 2025Review
- Construction and Interpretability of a Multimodal Deep Learning Model of Electronystagmography-Optical Coherence Tomography Angiography for Early Screening of Alzheimer's Disease.American journal of Alzheimer's disease and other dementiasArticle
- Sensory changes in preclinical, prodromal, and clinical Alzheimer's disease and related dementias.Alzheimer's & dementia (Amsterdam, Netherlands)Article
- Utilizing deep learning to predict Alzheimer's disease and mild cognitive impairment with optical coherence tomography.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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11 authors.
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Abstract
introductionDiagnostic performance of optical coherence tomography (OCT) to detect Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains limited. We aimed to develop a deep-learning algorithm using OCT to detect AD and MCI.
methodsWe performed a cross-sectional study involving 228 Asian participants (173 cases/55 controls) for model development and testing on 68 Asian (52 cases/16 controls) and 85 White (39 cases/46 controls) participants. Features from OCT were used to develop an ensemble trilateral deep-learning model.
resultsThe trilateral model significantly outperformed single non-deep learning models in Asian (area under the curve [AUC] = 0.91 vs. 0.71-0.72, DISCUSSION: Both multimodal approaches, using deep learning or traditional statistical models, show promise for AD and MCI detection. The choice between these models may depend on computational resources, interpretability preferences, and clinical needs. Highlights: A deep-learning algorithm was developed to detect Alzheimer's disease (AD) and mild cognitive impairment (MCI) using OCT images.The combined model outperformed single OCT parameters in both Asian and White cohorts.The study demonstrates the potential of OCT-based deep-learning algorithms for AD and MCI detection.
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