Comprehensive Review of Multimodal Techniques for Neurodegenerative Disease Diagnosis

neurodegenerative diseases, such as Alzheimer’s and Parkinson’s, represent a significant global health burden due to their progressive nature and lack of curative treatments. Early and accurate diagnosis is critical but remains a challenge due to the limitations of single-modality diagnostic tools. Recent advances in neuroimaging, genetic, and clinical data on these multimodal approaches have provided promising solutions by leveraging the complementary information from diverse biomarkers. This review revisits the state-of-the-art neurodegenerative disease-detection multimodal methods with regards to novel data fusion techniques, deep learning frameworks, and explainable AI applications. Key case studies and datasets are discussed that illustrate the impact of such methods in early diagnosis, cognitive monitoring, and the prediction of response to therapeutic intervention. Emerging trends to receive attention in this review include the incorporation of real-world data, and temporal biomarker analysis. Current challenges are addressed and future directions explored to further precision medicine in neurodegenerative disease diagnosis and management.