ArticleJournal of Alzheimer's disease : JAD2023
Can T1-Weighted Magnetic Resonance Imaging Significantly Improve Mini-Mental State Examination-Based Distinguishing Between Mild Cognitive Impairment and Early-Stage Alzheimer's Disease?
Article in Journal of Alzheimer's disease : JAD, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A computational model to describe multi-regional brain architecture during neurodegeneration in Alzheimer's disease.Scientific reports · 2026Article
- Comparing deep learning CNN method with traditional MRI-based hippocampal segmentation and volumetry for early Alzheimer's disease diagnosis across diverse populations.Scientific reports · 2025Article
- Lightweight Deep Learning Models with Explainable AI for Early Alzheimer's Detection from Standard MRI Scans.Diagnostics (Basel, Switzerland) · 2025Article
- Decoding Brain Development and Aging: Pioneering Insights From MRI Techniques.Investigative radiology · 2025Review
- Support Vector Machine for Stratification of Cognitive Impairment Using 3D T1WI in Patients with Type 2 Diabetes Mellitus.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
3 authors.
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Abstract
backgroundDetecting early-stage Alzheimer's disease (AD) is still problematic in clinical practice. This work aimed to find T1-weighted MRI-based markers for AD and mild cognitive impairment (MCI) to improve the screening process.
objectiveOur assumption was to build a screening model that would be accessible and easy to use for physicians in their daily clinical routine.
methodsThe multinomial logistic regression was used to detect status: AD, MCI, and normal control (NC) combined with the Bayesian information criterion for model selection. Several T1-weighted MRI-based radiomic features were considered explanatory variables in the prediction model.
resultsThe best radiomic predictor was the relative brain volume. The proposed method confirmed its quality by achieving a balanced accuracy of 95.18%, AUC of 93.25%, NPV of 97.93%, and PPV of 90.48% for classifying AD versus NC for the European DTI Study on Dementia (EDSD). The comparison of the two models: with the MMSE score only as an independent variable and corrected for the relative brain value and age, shows that the addition of the T1-weighted MRI-based biomarker improves the quality of MCI detection (AUC: 67.04% versus 71.08%) while maintaining quality for AD (AUC: 93.35% versus 93.25%). Additionally, among MCI patients predicted as AD inconsistently with the original diagnosis, 60% from ADNI and 76.47% from EDSD were re-diagnosed as AD within a 48-month follow-up. It shows that our model can detect AD patients a few years earlier than a standard medical diagnosis.
conclusionThe created method is non-invasive, inexpensive, clinically accessible, and efficiently supports AD/MCI screening.
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