Alzheimer disease predicting from clinical and MRI data using DeepALZNET dual pathway framework

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer's Disease (AD) is a significant neurological condition characterized by progressive cognitive deterioration, with prevalence rising exponentially with age. Currently, there is no effective cure, and the disease progression impacts patients' quality of life, often leading to severe symptoms before death. The gradual development of AD symptoms, often mistaken for typical aging, frequently leads to delayed diagnosis. This underscores the critical need for precise, early diagnostic methodologies, as timely intervention plays a crucial role in managing progression. Accordingly, this paper introduces DeepALZNET, a dual-pathway computational framework designed to enhance AD prediction by offering two independent processing pathways: one for structured clinical data and another for unstructured brain MRI scans. The first pathway combines a 1D Convolutional Neural Network (CNN) with a Random Forest classifier to analyze clinical data, while the second employs a transfer-learning-based VGG19 architecture to detect subtle structural changes in MRI scans. Empirical validation on two publicly available datasets (2k clinical cases and 40k MRI images) demonstrated that both pathways achieved competitive accuracy ([Formula: see text]), with further evaluation on ADNI and OASIS benchmarks confirming robustness. Unlike recent transformer-based or attention-driven methods, which often demand large multimodal datasets and high computational resources, DeepALZNET prioritizes practical applicability, interpretability, and adaptability, operating effectively with either clinical or imaging data alone. This design bridges the gap between benchmark-driven research and deployable real-world solutions, with potential for multimodal fusion or attention integration in future work.

Key findings

• Alzheimer's Disease (AD) is a significant neurological condition characterized by progressive cognitive deterioration, with prevalence rising exponentially with age.
• Empirical validation on two publicly available datasets (2k clinical cases and 40k MRI images) demonstrated that both pathways achieved competitive accuracy ([Formula: see text]), with further evaluation on ADNI and OASIS benchmarks confirming robustness.

Why it matters

This study introduces a dual-pathway computational framework for predicting Alzheimer's disease from clinical and MRI data and reports competitive accuracy on public datasets. It is a technical prediction study and does not test any treatment, cure, or prevention.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.1038/s41598-025-28221-0
• PubMed: 41345430
• Original source →
• Free full text: Scientific Reports →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{bekhet2025-41345430,
  author  = {Bekhet, Saddam and Saad, Nagwa Eid Sobhy and Farag, Sara},
  title   = {{Alzheimer disease predicting from clinical and MRI data using DeepALZNET dual pathway framework}},
  journal = {Scientific Reports},
  year    = {2025},
  doi     = {10.1038/s41598-025-28221-0},
  pmid    = {41345430},
  url     = {https://doi.org/10.1038/s41598-025-28221-0}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods Journal of Magnetic Resonance Imaging · 2008-02-26 · usefulness score 72/100 · Brain scans and imaging
• Neuropathological stageing of Alzheimer-related changes Acta Neuropathologica · 1991-09-01 · usefulness score 58/100 · Brain tissue and pathology
• Diagnosis of Early Alzheimer's Disease: Clinical Practice in 2021 The journal of prevention of Alzheimer's disease · 2021-01-01 · usefulness score 57/100 · Getting a diagnosis
• Early-Stage Alzheimer's Disease Prediction Using Machine Learning Models Frontiers in public health · 2022-03-03 · usefulness score 53/100 · Digital health tools
• OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease medRxiv · 2019-12-15 · usefulness score 16/100 · Genetics

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