ArticleBrain informatics2022
Early detection of Alzheimer's disease using neuropsychological tests: a predict-diagnose approach using neural networks.
Article in Brain informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Amyloid-beta statuses prediction with free water MR imaging features in Alzheimer's disease using machine learning models.BMC medical imaging · 2026Article
- Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians.Alzheimer's research & therapy · 2024Review
- Predicting Amyloid-β Positivity in Alzheimer's Disease: Comprehensive Analysis of Feature Selection and Machine Learning Models for Accurate Identification.International journal of online and biomedical engineering · 2023Article
- Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers.Sensors (Basel, Switzerland) · 2023Article
- A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer's disease.Brain informatics · 2023Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a slowly progressing disease for which there is no known therapeutic cure at present. Ongoing research around the world is actively engaged in the quest for identifying markers that can help predict the future cognitive state of individuals so that measures can be taken to prevent the onset or arrest the progression of the disease. Researchers are interested in both biological and neuropsychological markers that can serve as good predictors of the future cognitive state of individuals. The goal of this study is to identify non-invasive, inexpensive markers and develop neural network models that learn the relationship between those markers and the future cognitive state. To that end, we use the renowned Alzheimer's Disease Neuroimaging Initiative (ADNI) data for a handful of neuropsychological tests to train Recurrent Neural Network (RNN) models to predict future neuropsychological test results and Multi-Level Perceptron (MLP) models to diagnose the future cognitive states of trial participants based on those predicted results. The results demonstrate that the predicted cognitive states match the actual cognitive states of ADNI test subjects with a high level of accuracy. Therefore, this novel two-step technique can serve as an effective tool for the prediction of Alzheimer's disease progression. The reliance of the results on inexpensive, non-invasive tests implies that this technique can be used in countries around the world including those with limited financial resources.
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