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Artificial Intelligence in Alzheimer’s Disease Diagnosis
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Artificial Intelligence in Alzheimer’s Disease Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 11644

Special Issue Editor

Prof. Dr. Mohammad Shahadat Hossain

E-Mail Website
Guest Editor
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; brain informatics; Alzheimer’s disease; machine learning; explainable AI; soft computing; pervasive computing

Special Issue Information

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 for developing effective treatments and improving the quality of life for patients. The scope of artificial intelligence (AI) in diagnosing Alzheimer's disease is vast and promising because of its advancements in various areas, including learning, reasoning, and explainability. AI has demonstrated the ability to predict the likelihood of developing Alzheimer's disease. AI systems are promising in detecting early signs of this disease by analyzing patterns and anomalies in large data sets. Furthermore, AI can be used to track the progression of this disease by using the patient's cognitive function over time. The aim of this Special Issue is to consider novel AI research that has 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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (4 papers)

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Research

15 pages, 2987 KiB  
Article
Classification Prediction of Alzheimer’s Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images
by Yu-Ching Ni, Zhi-Kun Lin, Chen-Han Cheng, Ming-Chyi Pai, Pai-Yi Chiu, Chiung-Chih Chang, Ya-Ting Chang, Guang-Uei Hung, Kun-Ju Lin, Ing-Tsung Hsiao, Chia-Yu Lin and Hui-Chieh Yang
Diagnostics 2024, 14(4), 365; https://doi.org/10.3390/diagnostics14040365 - 7 Feb 2024
Viewed by 1431
Abstract
Alzheimer’s disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical dementia diagnosis [...] Read more.
Alzheimer’s disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical dementia diagnosis in Taiwan will also include Tc-99m-ECD SPECT imaging examination. Through machine learning and deep learning technology, we explored the feasibility of using the above clinical practice data to distinguish AD and VaD. We used the physiological data (33 features) and Tc-99m-ECD SPECT images of 112 AD patients and 85 VaD patients in the Taiwanese Nuclear Medicine Brain Image Database to train the classification model. The results, after filtering by the number of SVM RFE 5-fold features, show that the average accuracy of physiological data in distinguishing AD/VaD is 81.22% and the AUC is 0.836; the average accuracy of training images using the Inception V3 model is 85% and the AUC is 0.95. Finally, Grad-CAM heatmap was used to visualize the areas of concern of the model and compared with the SPM analysis method to further understand the differences. This research method can quickly use machine learning and deep learning models to automatically extract image features based on a small amount of general clinical data to objectively distinguish AD and VaD. Full article
(This article belongs to the Special Issue Artificial Intelligence in Alzheimer’s Disease Diagnosis)
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24 pages, 8713 KiB  
Article
An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning
by Tanjim Mahmud, Koushick Barua, Sultana Umme Habiba, Nahed Sharmen, Mohammad Shahadat Hossain and Karl Andersson
Diagnostics 2024, 14(3), 345; https://doi.org/10.3390/diagnostics14030345 - 5 Feb 2024
Cited by 15 | Viewed by 4192
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of individuals worldwide, causing severe cognitive decline and memory impairment. The early and accurate diagnosis of AD is crucial for effective intervention and disease management. In recent years, deep learning techniques have [...] Read more.
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of individuals worldwide, causing severe cognitive decline and memory impairment. The early and accurate diagnosis of AD is crucial for effective intervention and disease management. In recent years, deep learning techniques have shown promising results in medical image analysis, including AD diagnosis from neuroimaging data. However, the lack of interpretability in deep learning models hinders their adoption in clinical settings, where explainability is essential for gaining trust and acceptance from healthcare professionals. In this study, we propose an explainable AI (XAI)-based approach for the diagnosis of Alzheimer’s disease, leveraging the power of deep transfer learning and ensemble modeling. The proposed framework aims to enhance the interpretability of deep learning models by incorporating XAI techniques, allowing clinicians to understand the decision-making process and providing valuable insights into disease diagnosis. By leveraging popular pre-trained convolutional neural networks (CNNs) such as VGG16, VGG19, DenseNet169, and DenseNet201, we conducted extensive experiments to evaluate their individual performances on a comprehensive dataset. The proposed ensembles, Ensemble-1 (VGG16 and VGG19) and Ensemble-2 (DenseNet169 and DenseNet201), demonstrated superior accuracy, precision, recall, and F1 scores compared to individual models, reaching up to 95%. In order to enhance interpretability and transparency in Alzheimer’s diagnosis, we introduced a novel model achieving an impressive accuracy of 96%. This model incorporates explainable AI techniques, including saliency maps and grad-CAM (gradient-weighted class activation mapping). The integration of these techniques not only contributes to the model’s exceptional accuracy but also provides clinicians and researchers with visual insights into the neural regions influencing the diagnosis. Our findings showcase the potential of combining deep transfer learning with explainable AI in the realm of Alzheimer’s disease diagnosis, paving the way for more interpretable and clinically relevant AI models in healthcare. Full article
(This article belongs to the Special Issue Artificial Intelligence in Alzheimer’s Disease Diagnosis)
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21 pages, 2509 KiB  
Article
Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches
by Samra Shahzadi, Naveed Anwer Butt, Muhammad Usman Sana, Iñaki Elío Pascual, Mercedes Briones Urbano, Isabel de la Torre Díez and Imran Ashraf
Diagnostics 2023, 13(18), 2871; https://doi.org/10.3390/diagnostics13182871 - 7 Sep 2023
Cited by 1 | Viewed by 1423
Abstract
This study sought to investigate how different brain regions are affected by Alzheimer’s disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage [...] Read more.
This study sought to investigate how different brain regions are affected by Alzheimer’s disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage of Alzheimer’s disease (AD), six in the moderate stage, and six in the severe stage. The precuneus, cuneus, middle frontal gyri, calcarine cortex, superior medial frontal gyri, and superior frontal gyri were the areas impacted at all phases. A general linear model (GLM) is used to extract the voxels of the previously mentioned regions. The resting fMRI data for 18 AD patients who had advanced from MCI to stage 3 of the disease were obtained from the ADNI public source database. The subjects include eight women and ten men. The voxel dataset is used to train and test ten machine learning algorithms to categorize the MCI, mild, moderate, and severe stages of Alzheimer’s disease. The accuracy, recall, precision, and F1 score were used as conventional scoring measures to evaluate the classification outcomes. AdaBoost fared better than the other algorithms and obtained a phenomenal accuracy of 98.61%, precision of 99.00%, and recall and F1 scores of 98.00% each. Full article
(This article belongs to the Special Issue Artificial Intelligence in Alzheimer’s Disease Diagnosis)
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22 pages, 5825 KiB  
Article
Automatic Analysis of MRI Images for Early Prediction of Alzheimer’s Disease Stages Based on Hybrid Features of CNN and Handcrafted Features
by Ahmed Khalid, Ebrahim Mohammed Senan, Khalil Al-Wagih, Mamoun Mohammad Ali Al-Azzam and Ziad Mohammad Alkhraisha
Diagnostics 2023, 13(9), 1654; https://doi.org/10.3390/diagnostics13091654 - 8 May 2023
Cited by 19 | Viewed by 2687
Abstract
Alzheimer’s disease (AD) is considered one of the challenges facing health care in the modern century; until now, there has been no effective treatment to cure it, but there are drugs to slow its progression. Therefore, early detection of Alzheimer’s is vital to [...] Read more.
Alzheimer’s disease (AD) is considered one of the challenges facing health care in the modern century; until now, there has been no effective treatment to cure it, but there are drugs to slow its progression. Therefore, early detection of Alzheimer’s is vital to take needful measures before it develops into brain damage which cannot be treated. Magnetic resonance imaging (MRI) techniques have contributed to the diagnosis and prediction of its progression. MRI images require highly experienced doctors and radiologists, and the analysis of MRI images takes time to analyze each slice. Thus, deep learning techniques play a vital role in analyzing a huge amount of MRI images with high accuracy to detect Alzheimer’s and predict its progression. Because of the similarities in the characteristics of the early stages of Alzheimer’s, this study aimed to extract the features in several methods and integrate the features extracted from more than one method into the same features matrix. This study contributed to the development of three methodologies, each with two systems, with all systems aimed at achieving satisfactory accuracy for the detection of AD and predicting the stages of its progression. The first methodology is by Feed Forward Neural Network (FFNN) with the features of GoogLeNet and DenseNet-121 models separately. The second methodology is by FFNN network with combined features between GoogLeNet and Dense-121 models before and after high-dimensionality reduction of features using the Principal Component Analysis (PCA) algorithm. The third methodology is by FFNN network with combined features between GoogLeNet and Dense-121 models separately and features extracted by Discrete Wavelet Transform (DWT), Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods called handcrafted features. All systems yielded super results in detecting AD and predicting the stages of its progression. With the combined features of the DenseNet-121 and handcrafted, the FFNN achieved an accuracy of 99.7%, sensitivity of 99.64%, AUC of 99.56%, precision of 99.63%, and a specificity of 99.67%. Full article
(This article belongs to the Special Issue Artificial Intelligence in Alzheimer’s Disease Diagnosis)
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