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Predicting Mild Cognitive Impairment to Alzheimer's Disease Progression by Explainable Multi‐Modal Framework Using Deep and Machine Learning Hybrid Model
Soheil Zarei, Mohsen Saffar, Peyman Hassani‐Abharian, Reza Shalbaf, Ahmad Shalbaf. International Journal of Imaging Systems and Technology, 2026-06-09
Topic: Amyloid, tau and blood-test diagnosis
ABSTRACT Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is essential for early intervention and effective disease management. This study introduces an interpretable multi‐modal framework that integrates structural Magnetic Resonance Imaging (sMRI) features and neurocognitive clinical assessments through a hybrid deep learning and machine learning approach to predict progressive MCI (pMCI) conversion up to 3 years in advance. Cross‐sectional data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were employed. Spatial features were extracted from sMRI scans using a three‐dimensional convolutional neural network (3D CNN), while statistically selected neurocognitive assessments constituted the clinical feature set. These features were fused and classified using machine learning models, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM). Model interpretability was evaluated using SHapley Additive Explanations (SHAP). The proposed multi‐modal framework achieved a classification accuracy of 88.11%, an area under the curve (AUC) of 0.889, a precision of 82.33%, a sensitivity of 90.52%, and a specificity of 78.59%. The GBM classifier demonstrated superior performance by effectively integrating imaging and clinical features. SHAP analysis identified critical biomarkers influencing prediction outcomes, and the model exhibited strong generalizability across diverse clinical sites and imaging protocols. Consequently, this study presents a robust and interpretable framework for the early prediction of MCI conversion to AD. By combining deep learning‐derived imaging features with neurocognitive assessments, the proposed method offers a promising tool to support early diagnosis and targeted intervention strategies in clinical practice.
Key findings
- The GBM classifier demonstrated superior performance by effectively integrating imaging and clinical features.
Why it matters
A hybrid deep learning and machine learning framework combining structural MRI and neurocognitive assessments predicted conversion from mild cognitive impairment to Alzheimer's disease up to three years in advance with 88.11% accuracy. This is a prediction tool, not a treatment, cure or prevention.
Study details
- Effect size88.11%; 82.33%; 90.52%
- Evidence strengthLow
How this study scored 35/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (International Journal of Imaging Systems and Technology) |
| Journal's citation record | 1 / 4 | International Journal of Imaging Systems and Technology: journal h-index 77 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.5 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 3 / 6 | Cited 4.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Ahmad Shalbaf: h-index 27 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Shahid Beheshti University of Medical Sciences: institution h-index 381 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 35 / 100 | — |
What each of these 20 components measures, and what this score is not →
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Zarei, S., Saffar, M., Hassani‐Abharian, P., Shalbaf, R., & Shalbaf, A. (2026). 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. https://doi.org/10.1002/ima.70390
Vancouver
Zarei S, Saffar M, Hassani‐Abharian P, Shalbaf R, Shalbaf A. 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. doi:10.1002/ima.70390.
BibTeX
@article{zarei2026-b3a80709,
author = {Zarei, Soheil and Saffar, Mohsen and Hassani‐Abharian, Peyman and Shalbaf, Reza and Shalbaf, Ahmad},
title = {{Predicting Mild Cognitive Impairment to Alzheimer's Disease Progression by Explainable Multi‐Modal Framework Using Deep and Machine Learning Hybrid Model}},
journal = {International Journal of Imaging Systems and Technology},
year = {2026},
doi = {10.1002/ima.70390},
url = {https://doi.org/10.1002/ima.70390}
}
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