A Study on Artificial Intelligence and its Role in Redefining Alzheimer’s Disease Diagnostics
Keywords:
AD (Alzheimer's Disease), CNN (Convolutional Neural Networks), MRI (Magnetic resonance imaging), PET (Positron Emission Tomography)Abstract
This study Examines the use of ‘Artificial intelligence (AI)’ in developing revolutionary diagnostic approaches for Alzheimer's Disease (AD). Given the rising incidence of AD and the critical need for early and accurate diagnosis, AI presents promising solutions through advanced data analysis and pattern recognition.[1] This research reviews recent advancements in AI methodologies, including Deep learning frameworks, Convolutional Neural Networks (CNN), and explainable AI (XAI), applied to multimodal medical imaging such as MRI and PET scans. The study examines various datasets, evaluation metrics, and future directions in AI-based diagnostics, emphasizing the potential for integrating volumetric analysis, hyperparameter optimization, and multi-class classification to enhance diagnostic precision. The findings indicate that AI has the capability to significantly improve early detection, classification, and progression prediction of AD, offering valuable insights for clinical applications and the development of new therapeutic strategies.[12]
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References
Arafa, D. A., Moustafa, H. E. D., Ali, H. A., & Ali-Eldin, A. M. T. (2024). A deep learning framework for early diagnosis of Alzheimer’s disease on MRI images. Multimedia Tools and Applications, 83(2). Springer US. https://doi.org/10.1007/s11042-023-15738-7
Arafa, D. A., Moustafa, H. E. D., Ali-Eldin, A. M. T., & Ali, H. A. (2022). Early detection of Alzheimer’s disease based on the state-of-the-art deep learning approach: A comprehensive survey. Multimedia Tools and Applications.
Castellano, G., Esposito, A., Lella, E., Montanaro, G., & Vessio, G. (2024). Automated detection of Alzheimer’s disease: A multi-modal approach with 3D MRI and amyloid PET. Scientific Reports, 14(1), 1–10. https://doi.org/10.1038/s41598-024-56001-9
El-Assy, A. M., Amer, H. M., Ibrahim, H. M., & Mohamed, M. A. (2024). A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data. Scientific Reports, 14(1), 1–19. https://doi.org/10.1038/s41598-024-53733-6
Hasan, M. E., & Wagler, A. (2024). New convolutional neural network and graph convolutional network-based architecture for AI applications in Alzheimer’s disease and dementia-stage classification. Ai, 5(1), 342–363. https://doi.org/10.3390/ai5010017
Jack, C. R., & Holtzman, D. M. (2013). Biomarker modeling of Alzheimer's disease. Neuron, 80(6), 1347-1358.
Lu, P., Hu, L., Mitelpunkt, A., Bhatnagar, S., Lu, L., & Liang, H. (2024). A hierarchical attention-based multimodal fusion framework for predicting the progression of Alzheimer’s disease. Biomedical Signal Processing and Control, 88, 105669. https://doi.org/10.1016/j.bspc.2023.105669
Mahim, S. M., et al. (2023). Unlocking the potential of XAI for improved Alzheimer’s disease detection and classification using a ViT-GRU model. IEEE Access, 12, 8390–8412. https://doi.org/10.1109/ACCESS.2024.3351809
Odusami, M., Maskeliūnas, R., Damaševičius, R., & Misra, S. (2023). Explainable deep-learning-based diagnosis of Alzheimer’s disease using multimodal input fusion of PET and MRI images. Journal of M edical and Biological Engineering, 43(3), 291–302. https://doi.org/10.1007/s40846-023-00801-3
Shukla, A., Tiwari, R., & Tiwari, S. (2023). Alzheimer’s disease detection from fused PET and MRI modalities using an ensemble classifier. Machine Learning and Knowledge Extraction, 5(2), 512–538. https://doi.org/10.3390/make5020031
Sorour, S. E., El-Mageed, A. A. A., Albarrak, K. M., Alnaim, A. K., Wafa, A. A., & El-Shafeiy, E. (2024). Classification of Alzheimer’s disease using MRI data based on deep learning techniques. Journal of King Saud University - Computer and Information Sciences, 36(2), 101940. https://doi.org/10.1016/j.jksuci.2024.101940
Wang, L., et al. (2023). A metabolism-functional connectome sparse coupling method to reveal imaging markers for Alzheimer’s disease based on simultaneous PET/MRI scans. Human Brain Mapping, 44(17), 6020–6030. https://doi.org/10.1002/hbm.26493