Early Detection of Alzheimer's Disease through Speech-Text Analysis: A Review

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that poses a significant challenge for early diagnosis and effective treatment. While Magnetic Resonance Imaging (MRI) scans are commonly used for diagnosing AD, they often detect the disease at a later stage when brain damage has already occurred, limiting their effectiveness for early detection. In contrast, non-invasive diagnostic methods, particularly those based on speech analysis, have shown potential for identifying AD in its early stages. This review paper examines the role of machine learning techniques in analyzing speech patterns to detect early signs of AD. It provides a comparative analysis of recent studies, focusing on models like Transformers, Wav2Vec, and BERT, and their performance across various datasets such as ADReSSo and DementiaBank Pitt. The review highlights how the integration of advanced pre-processing techniques with deep learning architectures enhances diagnostic accuracy, with certain models achieving notably high performance. These findings emphasize the promise of AI-driven speech analysis as a cost-effective, scalable, and accessible approach for early detection, offering a critical window for timely intervention. While promising, challenges such as dataset diversity, computational efficiency, and clinical integration remain, which could affect the scalability and real-world applicability of these models. Further research is needed to address these limitations, refine the techniques, and explore their potential for improving diagnostic precision across diverse populations. Additionally, future work should focus on the integration of these models into clinical workflows and the exploration of longitudinal data to track AD progression over time, ultimately leading to more personalized and timely interventions. This could lead to better understanding of AD progression and the development of more personalized treatment strategies.