Artificial intelligence-based diagnosis of Alzheimer's disease with brain MRI images

Dominating Alzheimer's disease diagnosis with deep learning on sMRI and DTI-MD

Background

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has become one of the major health concerns for the elderly. Computer-aided AD diagnosis can assist doctors in quickly and accurately determining patients' severity and affected regions.

Methods

In this paper, we propose a method called MADNet for computer-aided AD diagnosis using multimodal datasets. The method selects ResNet-10 as the backbone network, with dual-branch parallel extraction of discriminative features for AD classification. It incorporates long-range dependencies modeling using attention scores in the decision-making layer and fuses the features based on their importance across modalities. To validate the effectiveness of our proposed multimodal classification method, we construct a multimodal dataset based on the publicly available ADNI dataset and a collected XWNI dataset, which includes examples of AD, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN).

Results

On this dataset, we conduct binary classification experiments of AD vs. CN and MCI vs. CN, and demonstrate that our proposed method outperforms other traditional single-modal deep learning models. Furthermore, this conclusion also confirms the necessity of using multimodal sMRI and DTI data for computer-aided AD diagnosis, as these two modalities complement and convey information to each other. We visualize the feature maps extracted by MADNet using Grad-CAM, generating heatmaps that guide doctors' attention to important regions in patients' sMRI, which play a crucial role in the development of AD, establishing trust between human experts and machine learning models.

Conclusion

We propose a simple yet effective multimodal deep convolutional neural network model MADNet that outperforms traditional deep learning methods that use a single-modality dataset for AD diagnosis.

Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization

The threat posed by Alzheimer's disease (AD) to human health has grown significantly. However, the precise diagnosis and classification of AD stages remain a challenge. Neuroimaging methods such as structural magnetic resonance imaging (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to diagnose and categorize AD. However, feature selection approaches that are frequently used to extract additional data from multimodal imaging are prone to errors. This paper suggests using a static pulse-coupled neural network and a Laplacian pyramid to combine sMRI and FDG-PET data. After that, the fused images are used to train the Mobile Vision Transformer (MViT), optimized with Pareto-Optimal Quantum Dynamic Optimization for Neural Architecture Search, while the fused images are augmented to avoid overfitting and then classify unfused MRI and FDG-PET images obtained from the AD Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets into various stages of AD. The architectural hyperparameters of MViT are optimized using Quantum Dynamic Optimization, which ensures a Pareto-optimal solution. The Peak Signal-to-Noise Ratio (PSNR), the Mean Squared Error (MSE), and the Structured Similarity Indexing Method (SSIM) are used to measure the quality of the fused image. We found that the fused image was consistent in all metrics, having 0.64 SIMM, 35.60 PSNR, and 0.21 MSE for the FDG-PET image. In the classification of AD vs. cognitive normal (CN), AD vs. mild cognitive impairment (MCI), and CN vs. MCI, the precision of the proposed method is 94.73%, 92.98% and 89.36%, respectively. The sensitivity is 90. 70%, 90. 70%, and 90. 91% while the specificity is 100%, 100%, and 85. 71%, respectively, in the ADNI MRI test data.

Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment

Artificial intelligence (AI) has emerged as a powerful tool in the field of neurology, significantly impacting the diagnosis and treatment of neurological disorders. Recent technological breakthroughs have given us access to a plethora of information relevant to many aspects of neurology. Neuroscience and AI share a long history of collaboration. Along with great potential, we encounter obstacles relating to data quality, ethics, and inherent difficulty in applying data science in healthcare. Neurological disorders pose intricate challenges due to their complex manifestations and variability. Automating image interpretation tasks, AI algorithms accurately identify brain structures and detect abnormalities. This accelerates diagnosis and reduces the workload on medical professionals. Treatment optimization benefits from AI simulations that model different scenarios and predict outcomes. These AI systems can currently perform many of the sophisticated perceptual and cognitive capacities of biological systems, such as object identification and decision making. Furthermore, AI is rapidly being used as a tool in neuroscience research, altering our understanding of brain functioning. It has the ability to revolutionize healthcare as we know it into a system in which humans and robots collaborate to deliver better care for our patients. Image analysis activities such as recognizing particular brain regions, calculating changes in brain volume over time, and detecting abnormalities in brain scans can be automated by AI systems. This lessens the strain on radiologists and neurologists while improving diagnostic accuracy and efficiency. It is now obvious that cutting-edge artificial intelligence models combined with high-quality clinical data will lead to enhanced prognostic and diagnostic models in neurological illness, permitting expert-level clinical decision aids across healthcare settings. In conclusion, AI's integration into neurology has revolutionized diagnosis, treatment, and research. As AI technologies advance, they promise to unravel the complexities of neurological disorders further, leading to improved patient care and quality of life. The symbiosis of AI and neurology offers a glimpse into a future where innovation and compassion converge to reshape neurological healthcare. This abstract provides a concise overview of the role of AI in neurology and its transformative potential.

Artificial intelligence for drug discovery and development in Alzheimer's disease

The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) analysis contributes to the understanding of the pathophysiology and precision medicine of the disease, including AD and AD-related dementia. In this review, we summarize the AI-driven methodologies for AD-agnostic drug discovery and development, including de novo drug design, virtual screening, and prediction of drug-target interactions, all of which have shown potentials. In particular, AI-based drug repurposing emerges as a compelling strategy to identify new indications for existing drugs for AD. We provide several emerging AD targets from human genetics and multi-omics findings and highlight recent AI-based technologies and their applications in drug discovery using AD as a prototypical example. In closing, we discuss future challenges and directions in AI-based drug discovery for AD and other neurodegenerative diseases.

Impact of AI-Powered Solutions in Rehabilitation Process: Recent Improvements and Future Trends

Rehabilitation is an important and necessary part of local and global healthcare services along with treatment and palliative care, prevention of disease, and promotion of good health. The rehabilitation process helps older and young adults even children to become as independent as possible in activities of daily life and enables participation in useful living activities, recreation, work, and education. The technology of Artificial Intelligence (AI) has evolved significantly in recent years. Many activities related to rehabilitation have been getting benefits from using AI techniques. The objective of this review study is to explore the advantages of AI for rehabilitation and how AI is impacting the rehabilitation process. This study aims at the most critical aspects of the rehabilitation process that could potentially take advantage of AI techniques including personalized rehabilitation apps, rehabilitation through assistance, rehabilitation for neurological disorders, rehabilitation for developmental disorders, virtual reality rehabilitation, rehabilitation of neurodegenerative diseases and Telerehabilitation of Cardiovascular. We presented a survey on the newest empirical studies available in the literature including the AI-based technology helpful in the Rehabilitation process. The novelty feature included but was not limited to an overview of the technological solutions useful in rehabilitation. Seven different categories were identified. Illustrative examples of practical applications were detailed. Implications of the findings for both research and practice were critically discussed. Most of the AI applications in these rehabilitation types are in their infancy and continue to grow while exploring new opportunities. Therefore, we investigate the role of AI technology in rehabilitation processes. In addition, we do statistical analysis of the selected studies to highlight the significance of this review work. In the end, we also present a discussion on some challenges, and future research directions.

A hybrid multimodal machine learning model for Detecting Alzheimer's disease

Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has limitations. Multimodal fusion of complementary biomarkers may improve diagnostic performance. This study proposes a multimodal machine learning framework integrating magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF) assays for enhanced AD characterization. The model incorporates a hybrid algorithm combining enhanced Harris Hawks Optimization (HHO) algorithm referred to as ILHHO, with Kernel Extreme Learning Machine (KELM) classifier for simultaneous feature selection and classification. ILHHO enhances HHO's search efficiency by integrating iterative mapping (IM) to improve population diversity and local escaping operator (LEO) to balance exploration-exploitation. Comparative analysis with other improved HHO algorithms, classic meta-heuristic algorithms (MHAs), and state-of-the-art MHAs on IEEE CEC2014 benchmark functions indicates that ILHHO achieves superior optimization performance compared to other comparative algorithms. The synergistic ILHHO-KELM model is evaluated on 202 AD Neuroimaging Initiative (ADNI) subjects. Results demonstrate superior multimodal classification accuracy over single modalities, validating the importance of fusing heterogeneous biomarkers. MRI + PET + CSF achieves 99.2 % accuracy for AD vs. normal control (NC), outperforming conventional and proposed methods. Discriminative feature analysis provides further insights into differential AD-related neurodegeneration patterns detected by MRI and PET. The differential PET and MRI features demonstrate how the two modalities provide complementary biomarkers. The neuroanatomical relevance of selected features supports ILHHO-KELM's potential for extracting sensitive AD imaging signatures. Overall, the study showcases the advantages of capitalizing on complementary multimodal data through advanced feature learning techniques for improving AD diagnosis.

Elossaily G, Vellapandian C, Prajapati B. Investigating the Potential Impact of Air Pollution on Alzheimer's Disease and the Utility of Multidimensional Imaging for Early Detection

Pollution is ubiquitous, and much of it is anthropogenic in nature, which is a severe risk factor not only for respiratory infections or asthma sufferers but also for Alzheimer's disease, which has received a lot of attention recently. This Review aims to investigate the primary environmental risk factors and their profound impact on Alzheimer's disease. It underscores the pivotal role of multidimensional imaging in early disease identification and prevention. Conducting a comprehensive review, we delved into a plethora of literature sources available through esteemed databases, including Science Direct, Google Scholar, Scopus, Cochrane, and PubMed. Our search strategy incorporated keywords such as "Alzheimer Disease", "Alzheimer's", "Dementia", "Oxidative Stress", and "Phytotherapy" in conjunction with "Criteria Pollutants", "Imaging", "Pathology", and "Particulate Matter". Alzheimer's disease is not only a result of complex biological factors but is exacerbated by the infiltration of airborne particles and gases that surreptitiously breach the nasal defenses to traverse the brain, akin to a Trojan horse. Various imaging modalities and noninvasive techniques have been harnessed to identify disease progression in its incipient stages. However, each imaging approach possesses inherent limitations, prompting exploration of a unified technique under a single umbrella. Multidimensional imaging stands as the linchpin for detecting and forestalling the relentless march of Alzheimer's disease. Given the intricate etiology of the condition, identifying a prospective candidate for Alzheimer's disease may take decades, rendering the development of a multimodal imaging technique an imperative. This research underscores the pressing need to recognize the chronic ramifications of invisible particulate matter and to advance our understanding of the insidious environmental factors that contribute to Alzheimer's disease.

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth's subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.

A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer's Disease

Alzheimer's disease (AD) is a progressive illness with a slow start that lasts many years; the disease's consequences are devastating to the patient and the patient's family. If detected early, the disease's impact and prognosis can be altered significantly. Blood biosamples are often employed in simple medical testing since they are cost-effective and easy to collect and analyze. This research provides a diagnostic model for Alzheimer's disease based on federated learning (FL) and hardware acceleration using blood biosamples. We used blood biosample datasets provided by the ADNI website to compare and evaluate the performance of our models. FL has been used to train a shared model without sharing local devices' raw data with a central server to preserve privacy. We developed a hardware acceleration approach for building our FL model so that we could speed up the training and testing procedures. The VHDL hardware description language and an Altera 10 GX FPGA are utilized to construct the hardware-accelerator approach. The results of the simulations reveal that the proposed methods achieve accuracy and sensitivity for early detection of 89% and 87%, respectively, while simultaneously requiring less time to train than other algorithms considered to be state-of-the-art. The proposed algorithms have a power consumption ranging from 35 to 39 mW, which qualifies them for use in limited devices. Furthermore, the result shows that the proposed method has a lower inference latency (61 ms) than the existing methods with fewer resources.