Multimodal Deep Learning for Early Prediction of Neurodegenerative Diseases: A Comprehensive Review

Abstract

Major neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis and Huntington's disease are characterized by the gradual degeneration or loss of brain neurons, which drastically affects memory, thinking and movement. Though the damaged neurons are irreversible, early detection may help to slow down the disease progression and improve therapeutic outcomes. In recent years, Deep Learning, a powerful field of AI, has proved to be an effective tool due to its ability to derive intricate patterns from substantial and varied biomedical datasets. This review compiles the current deep learning methods for the early detection of neurodegenerative disorders, based on structural and functional neuroimaging, EEG signals and genetic data. With a focus on accuracy, interpretability and clinical value, it examines the contribution of key models, including convolutional neural networks, transformers, graph neural networks and recurrent neural networks. Major issues like data asymmetry, the inability to create annotated datasets and reduced model generalizability across patient groups are also highlighted and discussed about future research directions that could pave the way for better early diagnosis and treatments.

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