Hippocampal atrophy based Alzheimer's disease diagnosis via machine learning methods

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer's disease is the most common form of dementia and is a serious health problem. The disease is expected to increase further in the upcoming years with the increase of the elderly population. Developing new treatments and diagnostic methods is getting more important. In this study, we focused on the early diagnosis of dementia in Alzheimer's disease via analysis of neuroimages. We analyzed the data diagnosed by the Alzheimer's Disease Neuroimaging Initiative (ADNI) protocol. The analyzed data were T1-weighted magnetic resonance images of 159 patients with Alzheimer's disease, 217 patients with mild cognitive impairment and 109 cognitively healthy older people. In this study, we propose that the volumetric reduction in the hippocampus is the most important indicator of Alzheimer's disease. There is not much research about the relationship between the volumetric reduction in the hippocampus and Alzheimer's disease. This volume information was calculated through semi-automatic segmentation software ITK-SNAP and a data set was created based on age, gender, diagnosis, and right and left hippocampal volume values. The diagnosis via hippocampal volume information was made by using machine learning techniques. By using this approach, we conclude that brain MRIs can be used to distinguish the patients with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) from each other; while most of the studies were only able to distinguish AD from CN. Our results have revealed that our approach improves the performance of the computer-aided diagnosis of Alzheimer's disease.

Key findings

• By using this approach, we conclude that brain MRIs can be used to distinguish the patients with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) from each other; while most of the studies were only able to distinguish AD from CN.
• Our results have revealed that our approach improves the performance of the computer-aided diagnosis of Alzheimer's disease.

Why it matters

This study used machine learning on MRI scans from 485 people and found that hippocampus volume can help distinguish Alzheimer's disease, mild cognitive impairment, and cognitively normal people from each other. It is about improving computer-aided diagnosis, not about treating, curing or preventing Alzheimer's disease.

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Study details

• Participants159
• Evidence strengthLow

Read the original

• DOI: 10.1016/j.jneumeth.2020.108669
• PubMed: 32126274
• Original source →

May need a subscription OpenAlex found no free copy of the full text (checked 6 Feb 1020), so the publisher may charge for it. The PubMed abstract is free.

@article{uysal2020-32126274,
  author  = {Uysal, G. and Ozturk, M.},
  title   = {{Hippocampal atrophy based Alzheimer's disease diagnosis via machine learning methods}},
  journal = {Journal of neuroscience methods},
  year    = {2020},
  doi     = {10.1016/j.jneumeth.2020.108669},
  pmid    = {32126274},
  url     = {https://doi.org/10.1016/j.jneumeth.2020.108669}
}

Studies this one cites

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

• Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment Neurology · 1999-04-01 · usefulness score 66/100 · Brain scans and imaging
• Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort Current Alzheimer research · 2009-08-01 · usefulness score 66/100 · Getting a diagnosis
• MRI and CSF studies in the early diagnosis of Alzheimer's disease Journal of internal medicine · 2004-09-01 · usefulness score 51/100 · Mild cognitive impairment
• Medial temporal lobe atrophy and memory dysfunction as predictors for dementia in subjects with mild cognitive impairment Journal of neurology · 1999-06-01 · usefulness score 47/100 · Mild cognitive impairment

Later studies that cite this one

1 study in the alzhub library lists this one in its references.

• Enhanced detection of mild cognitive impairment in Alzheimer’s disease: a hybrid model integrating dual biomarkers and advanced machine learning BMC Geriatrics · 2025-01-23 · usefulness score 43/100 · Mild cognitive impairment

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• Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation Scientific reports · 2018-03-07 · usefulness score 62/100

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• Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI Proceedings of the National Academy of Sciences · 2004-03-15 · usefulness score 58/100 · Brain scans and imaging
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