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Artificial Intelligence in Alzheimer’s Disease Diagnosis—2nd Edition

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A Special Issue of Diagnostics (ISSN 2075-4418) belonging to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 6638

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Prof. Dr. Mohammad Shahadat Hossain

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1. Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh
2. Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, Lulea, Sweden
Interests: artificial intelligence; expert systems; health informatics; barin informatics; Alzheimer’s disease; machine learning; explaianble AI; soft computing; pervasive computing
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Dear Colleagues,

Alzheimer’s disease is a neurodegenerative disorder that affects memory and other cognitive functions. It is also the fifth-leading cause of death in adults aged 65 and above. Therefore, the early detection and diagnosis of Alzheimer's disease are crucial in developing effective treatments and improving quality of life for patients. The scope of artificial intelligence (AI) in diagnosing Alzheimer's disease is vast and impressive, thanks to advancements in a range of areas, including learning, reasoning, and explainability. AI has demonstrated the ability to predict the likelihood of developing Alzheimer's disease. AI systems show promise in detecting the early signs of this disease by analyzing patterns and anomalies in large data sets. Furthermore, AI can be used to track the progression of the disease using the patient's cognitive function over time. Our aim for this Special Issue is to share novel research on AI systems that have been developed, implemented, and evaluated to support the prediction, early detection, and progression of Alzheimer’s disease over time, in accordance with the policy of the journal Diagnostics.

Prof. Dr. Mohammad Shahadat Hossain
Guest Editor

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Keywords

Alzheimer’s disease
artificial intelligence
machine learning
explainable AI
expert systems
computer vision
deep learning
diagnosis
cognition
brain informatics
neurodegenerative disorder

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Published Papers (6 papers)

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Research

23 pages, 8380 KB

Open AccessArticle

PCA-Enhanced Deep Features for Alzheimer’s Disease Stage Classification with EFMM

by Marwa Mawfaq Mohamedsheet Al-Hatab, Ruaa H. Ali Al-Mallah, Maysaloon Abed Qasim, Mohammed Falah Mohammed, Taha H. Rassem and Abdulghani Ali Ahmed

Diagnostics 2026, 16(15), 2428; https://doi.org/10.3390/diagnostics16152428 - 31 Jul 2026

Viewed by 411

Abstract

Background/Objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder necessitating accurate and timely diagnosis for effective clinical intervention. While deep learning methods have shown promise in AD classification, many rely on computationally intensive architectures and high-dimensional feature representations. This study introduces a lightweight hybrid framework combining deep feature extraction, dimensionality reduction, and adaptive classification for MRI-based Alzheimer’s disease stage classification. Methods: Utilizing MRI images from a publicly available Alzheimer’s disease dataset encompassing four clinical stages (Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented), deep features were extracted using a pre-trained SqueezeNet model as a fixed feature extractor, generating 1000-dimensional feature vectors. Due to the computational complexity and for the improvement of the model efficiency, the dimensionality reduction technique, Principal Component Analysis (PCA) was then applied. This resulted in an optimum representation of 100 principal components, retaining about 96% of the variance. Then, the performances of various machine learning classifiers such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Tree (DT), Neural Network (NN), Naïve Bayes (NB), Logistic Regression (LR) and Enhanced Fuzzy Min–Max Neural Network (EFMM) were tested. The accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC), and confusion matrices were used to evaluate the performance. Stratified 5-fold cross validation was used to ensure the strength of our results. Results: The findings show that PCA has a significant improvement in classification accuracy for most of the models. In particular, the EFMM classifier outperformed the other classifiers, with an accuracy of 97.19% on the independent test set. After PCA, the AUC values for classes such as Mild Demented, Moderate Demented, Non-Demented and Very Mild Demented were obtained as 97.12%, 99.97%, 93.79% and 95.26% respectively. We further validated our proposed framework using stratified 5-fold cross validation which further corroborated the robustness of our proposed framework. The EFMM achieved a mean accuracy of 98.38% ± 0.36 and a mean macro-F1 score of 98.48% ± 0.43. Friedman statistical testing demonstrated that there were significant differences between the performance of the classifiers evaluated (p < 0.001), which further validated the performance of the EFMM. Conclusions: To sum up, the proposed SqueezeNet–PCA–EFMM is an effective and efficient method for Alzheimer’s disease stage classification under MRI images. The combination of SqueezeNet, PCA, and EFMM—led not only to high classification performance, but also to good cross validation results. Furthermore, this property of incremental learning is the intrinsic one of the EFMM and renders this framework interesting for its incorporation in the next-generation intelligent clinical decision supports in particular, as medical care evolves. Full article

(This article belongs to the Special Issue Artificial Intelligence in Alzheimer’s Disease Diagnosis—2nd Edition)

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19 pages, 2530 KB

Open AccessArticle

Machine Learning-Based Multiclass Classification of Cognitive Stages Using Plasma Biomarkers, Clinical Assessments, and Genetic Features: A Repeated, Nested Cross-Validation Study in ADNI with External Evaluation in CNTN

by Jiayuan Xu and Fumie Costen

Diagnostics 2026, 16(12), 1755; https://doi.org/10.3390/diagnostics16121755 - 6 Jun 2026

Viewed by 546

Abstract

Background: Plasma biomarkers are promoted as scalable tools for the staging of Alzheimer’s disease (AD), yet head-to-head comparisons against the clinical scales used to define diagnostic labels remain scarce. Reported gains from machine learning fusion of clinical and biomarker features may reflect label circularity rather than biological signals, and quantifying this circularity is a central aim of the present work. Methods: From the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we assembled 655 participants (CN = 296, MCI = 168, and AD = 191) with concurrent plasma biomarkers (pT217, A

β

42/40, NfL, and GFAP), clinical scales (MMSE, CDR-SB, and FAQ), APOE genotype, and demographics. Three pre-specified feature sets (clinical-only, biomarker plus demographic–genetic, and full fusion) were compared across four classifiers (Logistic Regression, SVM, Random Forest, and XGBoost) using repeated, nested cross-validation (5-fold × 3 outer, 5-fold inner) with balanced class weighting. Because the external Center for Neurodegeneration and Translational Neuroscience (CNTN) cohort (

n = 130

) measures pT181 rather than pT217 and lacks A

β

42/40, external evaluation used a separate reduced feature panel (NfL, GFAP, APOE, age, sex, and education), not the proposed pT217-inclusive panel. Results: Clinical scales alone reached a three-class AUC-OVR of

0.9539 \pm 0.0041

, and fusion reached

0.9559 \pm 0.0046

, an indistinguishable gain. Because MMSE, CDR-SB, and FAQ partly determine ADNI diagnostic labels, both estimates are circularity-inflated upper bounds and do not reflect independent classification power. Independent of this circularity, the internal plasma plus demographic–genetic model still achieved AUC-OVR

= 0.7455 \pm 0.0150

, with pT217 as the dominant contributor. Pairwise discrimination was excellent for CN vs. AD (

1.0000

) and MCI vs. AD (

0.9739

) but markedly weaker for CN vs. MCI (

0.9302

for fused and

0.6972

for plasma only). The separate reduced-feature model, which contains neither pT217 nor A

β

42/40, transferred to CNTN with AUC-OVR

= 0.702

(95% CI

0.635

–

0.764

). Conclusions: Apparent fusion gains in ADNI are largely a consequence of label circularity. After removing the circular clinical features, the internal pT217-inclusive plasma model supports three-class CN/MCI/AD screening at AUC

\approx 0.74

and a reduced panel without pT217 transfers to an independent cohort at AUC

\approx 0.70

. These values provide a realistic performance estimate for blood-based AD staging under the current feature set, diagnostic label structure, and cohort design, and richer feature sets or pathology-anchored labels may shift this estimate. MCI detection remains the principal bottleneck. Full article

(This article belongs to the Special Issue Artificial Intelligence in Alzheimer’s Disease Diagnosis—2nd Edition)

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17 pages, 2337 KB

Open AccessArticle

Artificial Intelligence-Assisted Volumetric Brain Analysis Correlated with CSF Biomarkers in Alzheimer’s Disease: A Pilot Study

by Pukovisa Prawiroharjo, Amelia Nur Vidyanti, Yuliarni Syafrita, Reyhan Eddy Yunus, Aldithya Fakhri, Violine Martalia, Aileen Gabrielle, Sarah Alya Rahmayani, Gamael Marcel, Vidya Gani Wijaya and Alya Ayu Tazkia

Diagnostics 2026, 16(7), 1050; https://doi.org/10.3390/diagnostics16071050 - 31 Mar 2026

Viewed by 1079

Abstract

Background/Objectives: Alzheimer’s disease (AD) is a leading cause of dementia globally, yet standard diagnostic markers like cerebrospinal fluid (CSF) analysis and molecular imaging are invasive and resource-intensive. While artificial intelligence (AI)-based volumetric magnetic resonance imaging (MRI) offers a scalable and non-invasive alternative, data correlating these structural metrics with fluid biomarkers and cognitive status in Southeast Asian populations are scarce. This study addresses this critical gap by examining the within-cohort relationship between CSF biomarkers and regional brain volumes derived from AI-assisted MRI in Indonesian patients with clinically diagnosed AD, providing novel data for an underrepresented population. Methods: Twenty-one AD patients from three national referral hospitals in Indonesia underwent lumbar puncture for CSF biomarker analysis and 3 Tesla structural brain MRI. Brain volumes were analyzed using United Imaging Intelligence software, focusing on AD-relevant regions (hippocampus, entorhinal cortex, parahippocampus, precuneus, and posterior cingulate cortex [PCC]). Results: Spearman’s correlation revealed significant positive associations between CSF Aβ42 levels and several brain regions. Strong correlations were found with the right entorhinal volume indexed to intracranial volume (VICV) (r = 0.601, p = 0.004), right PCC VICV (r = 0.603, p = 0.004), right entorhinal volume (r = 0.533, p = 0.013), and right hippocampus VICV (r = 0.503, p = 0.020). Furthermore, MoCA-InA scores demonstrated highly significant positive correlations with CSF Aβ42 concentrations (r = 0.720, p < 0.001), right Hippocampus VICV (r = 0.703, p < 0.001), and right PCC VICV (r = 0.695, p < 0.001). No significant correlations were found between CSF pTau or the pTau/Aβ42 ratio and regional volumes. Conclusions: These results highlight the entorhinal cortex and PCC as early affected regions where CSF Aβ42 correlates with preserved volume, supporting their role as structural markers in early AD. The absence of pTau associations may reflect early-stage pathology or limitations of cross-sectional volumetry. In resource-limited settings, AI-assisted volumetric MRI demonstrates potential utility as a non-invasive tool for stratifying amyloid-associated brain atrophy and staging disease severity. Full article

(This article belongs to the Special Issue Artificial Intelligence in Alzheimer’s Disease Diagnosis—2nd Edition)

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27 pages, 3920 KB

Open AccessArticle

Deep Learning-Based Alzheimer’s Disease Detection from Multi-Channel EEG Using Fused Time–Frequency Image Grids

by Abdulnasır Yıldız and Hasan Zan

Diagnostics 2026, 16(5), 746; https://doi.org/10.3390/diagnostics16050746 - 2 Mar 2026

Cited by 1 | Viewed by 1281

Abstract

Background/Objectives: Dementia is a progressive neurodegenerative disorder for which accurate and timely diagnosis remains a major clinical challenge. Electroencephalography (EEG) offers a noninvasive and cost-effective means of capturing neurophysiological alterations, motivating the development of reliable EEG-based automated diagnostic frameworks. This study aims to systematically examine how different time–frequency representations (TFRs) affect dementia classification performance within a unified multi-channel EEG image fusion framework. Methods: Resting-state, eyes-closed EEG recordings from 88 subjects, including Alzheimer’s disease, frontotemporal dementia, and cognitively normal controls, were preprocessed and segmented. Channel-wise signals were converted into two-dimensional time–frequency images using Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), Hilbert–Huang Transform (HHT), Wigner–Ville Distribution (WVD), or Constant-Q Transform (CQT). Images from 19 EEG channels were fused into a structured grid and classified using pretrained convolutional neural networks, including MobileNetV2, ResNet-50, and InceptionV3. Results: Results indicate that classification performance is highly dependent on the chosen TFR. The STFT-based representation combined with InceptionV3 achieved the highest accuracy, reaching 98.8% with random splitting and 84.3% with subject-wise splitting, outperforming previous studies. CQT also showed competitive performance, whereas HHT and WVD were less effective. Gradient-weighted class activation mapping provided interpretable visualization of physiologically relevant EEG channel contributions. Conclusions: The proposed framework demonstrates the importance of structured multi-channel fusion and systematic TFR evaluation for robust and interpretable EEG-based dementia classification and serves as a foundation for future cross-dataset validation. Full article

(This article belongs to the Special Issue Artificial Intelligence in Alzheimer’s Disease Diagnosis—2nd Edition)

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22 pages, 6262 KB

Open AccessArticle

Progression-Aware and Explainable CNN–Transformer Framework for Multiclass Alzheimer’s Disease Staging Using MRI

by Khalaf Alsalem, Murtada K. Elbashir, Ahmed Omar Alzahrani, Mohanad Mohammed, Mahmood A. Mahmood and Tarek Abd El Fattah

Diagnostics 2026, 16(4), 593; https://doi.org/10.3390/diagnostics16040593 - 16 Feb 2026

Cited by 1 | Viewed by 1019

Abstract

Background: Alzheimer disease (AD) is a neurodegenerative condition that progressively develops structural changes in the brain, resulting in different stages of severity, which makes accurate multiclass classification from magnetic resonance imaging (MRI) challenging. Despite promising outcomes of deep learning models, a great number of current methods disregard disease progression, suffer from evaluation leakage, or lack interpretability. Objectives: This paper introduces DeepAttentionADNet, a lightweight hybrid CNN–Transformer framework designed for multiclass staging of Alzheimer’s disease using MRI images. Methods: The proposed model integrates convolutional feature extraction with transformer-based global context modeling. To capture the ordered nature of disease severity, a progression-aware ordinal learning objective is proposed. Moreover, consistency regularization is utilized to enhance robustness by imposing consistent prediction with spatial perturbation. A leakage-free k-fold cross-validation protocol is adopted, in which data splitting is performed prior to augmentation. Also, to promote interpretability, token-level importance maps based on transformer embeddings are utilized to visualize spatial regions that were used to make classification decisions. Results: The experimental findings on a multiclass MRI dataset of Alzheimer disease demonstrate consistent and high performance across cross-validation folds (mean F1-score (0.991 ± 0.003) and AUROC (0.9998 ± 0.0002)), without losing transparency and progress awareness. Conclusions: The proposed framework provided a robust and interpretable method of Alzheimer disease severity classification using MRI. Full article

(This article belongs to the Special Issue Artificial Intelligence in Alzheimer’s Disease Diagnosis—2nd Edition)

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16 pages, 1831 KB

Open AccessArticle

The ICN-UN Battery: A Machine Learning-Optimized Tool for Expeditious Alzheimer’s Disease Diagnosis

by Ernesto Barceló, Duban Romero, Ricardo Allegri, Eliana Meza, María I. Mosquera-Heredia, Oscar M. Vidal, Carlos Silvera-Redondo, Mauricio Arcos-Burgos, Pilar Garavito-Galofre and Jorge I. Vélez

Diagnostics 2025, 15(23), 3045; https://doi.org/10.3390/diagnostics15233045 - 28 Nov 2025

Viewed by 960

Abstract

Background/Objectives: Alzheimer’s disease (AD) accounts for ~70% of global dementia cases, with projections estimating 139 million affected individuals by 2050. This increasing burden highlights the urgent need for accessible, cost-effective diagnostic tools, particularly in low- and middle-income countries (LMICs). Traditional neuropsychological assessments, while effective, are resource-intensive and time-consuming. Methods: A total of 760 older adults (394 [51.8%] with AD) were recruited and neuropsychologically evaluated at the Instituto Colombiano de Neuropedagogía (ICN) in collaboration with Universidad del Norte (UN), Barranquilla. Machine learning (ML) algorithms were trained on a screening protocol incorporating demographic data and neuropsychological measures assessing memory, language, executive function, and praxis. Model performance was determined using 10-fold cross-validation. Variable importance analyses identified key predictors to develop optimized, abbreviated ML-based protocols. Metrics of compactness, cohesion, and separation further quantified diagnostic differentiation performance. Results: The eXtreme Gradient Boosting (xgbTree) algorithm achieved the highest diagnostic accuracy (91%) with the full protocol. Five ML-optimized screening protocols were also developed. The most efficient, the ICN-UN battery (including MMSE, Rey–Osterrieth Complex Figure recall, Rey Auditory Verbal Learning, Lawton & Brody Scale, and FAST), maintained strong diagnostic performance while reducing screening time from over four hours to under 25 min. Conclusions: The ML-optimized ICN-UN protocol offers a rapid, accurate, and scalable AD screening solution for LMICs. While promising for clinical adoption and earlier detection, further validation in diverse populations is recommended. Full article

(This article belongs to the Special Issue Artificial Intelligence in Alzheimer’s Disease Diagnosis—2nd Edition)

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