Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features

Topic: Risk, prevention and care in population studies

One of the most challenging tasks for neurologists is the early diagnosis of Alzheimer’s disease (AD). Early and accurate diagnosis of the mild cognitive impairment (MCI) stage can enhance efforts to slow down the major consequences linked to this condition. Deep learning systems provide a promising performance in diagnosing the disease through neuroimaging analysis. This research aims to develop a deep learning-based system that efficiently identifies and analyzes the progression from Cognitively Normal (CN) to MCI, addressing the growing need for more accessible, accurate diagnostic tools. The proposed model comprises two distinct feature extraction paths to capture local and global image features. Each path includes advanced modules for feature refinement associated with the channel attention mechanism. The resultant output features are produced using a learned fusion technique from the two paths’ features and applied to the CN vs. MCI binary classifier. Furthermore, the proposed Suspected Subject Classifier (SSC) system applies various machine-learning methods to identify the suspected MCI subjects. The results showed a comparative performance for the binary diagnosis of CN individuals and those with MCI, achieving an accuracy of 91.6% and 88.4% for multi-class diagnoses, including the prediction of progression from normal to confirmed MCI. This study represents an exceptional stride toward predicting early MCI in normal individuals. By enhancing prediction efficiency for early disease progression in normal individuals, our method can potentially advance intervention strategies and improve patient care outcomes.

Key findings

• Each path includes advanced modules for feature refinement associated with the channel attention mechanism.
• The results showed a comparative performance for the binary diagnosis of CN individuals and those with MCI, achieving an accuracy of 91.6% and 88.4% for multi-class diagnoses, including the prediction of progression from normal to confirmed MCI.

Why it matters

This study developed a deep learning system using MRI features that achieved 91.6% accuracy for distinguishing cognitively normal individuals from those with MCI and 88.4% for multi-class diagnoses including predicting progression from normal to confirmed MCI. The work is a diagnostic tool development and does not test any treatment or prevention.

Study details

• Effect size91.6%; 88.4%
• Evidence strengthLow

Read the original

• DOI: 10.1109/access.2025.3588801
• Original source →
• Free full text: IEEE Access →

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

@article{baktash2025-dec3cad3,
  author  = {Baktash, Abdullah Qassim and Sarbaz, Yashar and Meshgini, Saeed and Afrouzian, Reza},
  title   = {{Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features}},
  journal = {IEEE Access},
  year    = {2025},
  doi     = {10.1109/access.2025.3588801},
  url     = {https://doi.org/10.1109/access.2025.3588801}
}

Studies this one cites

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

• Dementia prevention, intervention, and care: 2020 report of the Lancet Commission The Lancet · 2020-07-30 · usefulness score 67/100 · Prevention and risk
• Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019 The Lancet Public Health · 2022-01-06 · usefulness score 52/100 · How common it is

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• Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer’s disease progression Alzheimer s Research & Therapy · 2025-08-07 · usefulness score 55/100
• Predicting Mild Cognitive Impairment to Alzheimer's Disease Progression by Explainable Multi‐Modal Framework Using Deep and Machine Learning Hybrid Model International Journal of Imaging Systems and Technology · 2026-06-09 · usefulness score 35/100
• Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers International Journal of Molecular Sciences · 2025-03-08 · usefulness score 48/100
• Predicting Alzheimer's disease progression using multi-modal deep learning approach Scientific reports · 2019-02-13 · usefulness score 66/100
• Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification PloS one · 2016-02-22 · usefulness score 55/100

Also tagged “assessment”

840 studies in the library carry the topic tag “assessment”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 840 tagged studies.

• Mild cognitive impairment: an opportunity to identify patients at high risk for progression to Alzheimer's disease Clinical therapeutics · 2006-07-01 · usefulness score 50/100 · Mild cognitive impairment
• Efficacy and Moderators of Virtual Reality for Cognitive Training in People with Dementia and Mild Cognitive Impairment: A Systematic Review and Meta-Analysis Journal of Alzheimer's disease : JAD · 2022-01-01 · usefulness score 67/100 · Prevention trials
• Chronic kidney disease and cognitive impairment: a systematic review and meta-analysis American journal of nephrology · 2012-05-03 · usefulness score 69/100 · Risk factors

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More on Mild cognitive impairment

• A review that covers this ground Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment The Cochrane database of systematic reviews · 2020-03-02 · usefulness score 71/100
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