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Review Open Access

Applications of machine learning and multimodal integration for the early diagnosis of neurodegenerative diseases (Review)

  • Authors:
    • Saranya Velmurugan
    • Shiek Waheeda
    • Langeswaran Kulanthaivel
    • Gowtham Kumar Subbaraj
  • View Affiliations / Copyright

    Affiliations: Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Chennai, Tamil Nadu 603103, India, Department of Physiology, Kirupananda Variyar Medical College and Hospital, Vinayaka Mission Research Foundation (Deemed to be University), Salem, Tamil Nadu 636308, India, Department of Biomedical Sciences, Alagappa University, Karaikudi, Tamil Nadu 630001, India
    Copyright: © Velmurugan et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY 4.0].
  • Article Number: 115
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    Published online on: October 3, 2025
       https://doi.org/10.3892/wasj.2025.403
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Abstract

Neurodegenerative disorders (NDDs) such as Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis are critical worldwide health issues. Recent diagnostic methods primarily rely on biomarkers and clinical evaluations, often exhibiting insufficient specificity and sensitivity during the initial stages of illness. The present review discusses the machine learning (ML) techniques used to enhance the early prediction and detection of NDDs. The use of ML in analyzing many data modalities, including genetic biomarkers, molecular and cellular biomarkers, neuroimaging data, and cognitive/behavioral evaluations is also discussed. Research with ML techniques, including convolutional neural networks, support vector machines and recurrent neural networks has demonstrated substantial improvements in diagnostic precision for numerous NDDs, often exceeding conventional methodologies. Moreover, multimodal integration techniques that integrate various types of data further enhance prediction power. However, despite the positive results, challenges such as data standardization, privacy concerns and the requirement for robust validation across numerous populations persist. Addressing these challenges will be crucial for translating the potential of ML into clinically impactful tools for the early diagnosis, personalized treatment and improved management of NDDs.
View Figures

Figure 1

General pathophysiological factors of
neurodegenerative diseases. SNCA, synuclein alpha.

Figure 2

Overview of artificial intelligence
and machine learning techniques. ML, machine learning.

Figure 3

Identification of genetic, molecular,
and cellular biomarkers using machine learning models. SNPs, single
nucleotide polymorphisms; GWAS, genome-wide association studies;
SVM, support vector machine; CNNs, conventional neural
networks.

Figure 4

Schematic diagram illustrating how
machine learning models are used in the early detection of
cognitive and behavioral assessment. SVM, support vector machine;
ANN, artificial neural network; OLS, ordinary least squares; LR,
linear regression; MRI, magnetic resonance imaging; PET, positron
emission tomography; FMRI, functional magnetic resonance
imaging.
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Spandidos Publications style
Velmurugan S, Waheeda S, Kulanthaivel L and Subbaraj GK: Applications of machine learning and multimodal integration for the early diagnosis of neurodegenerative diseases (Review). World Acad Sci J 7: 115, 2025.
APA
Velmurugan, S., Waheeda, S., Kulanthaivel, L., & Subbaraj, G.K. (2025). Applications of machine learning and multimodal integration for the early diagnosis of neurodegenerative diseases (Review). World Academy of Sciences Journal, 7, 115. https://doi.org/10.3892/wasj.2025.403
MLA
Velmurugan, S., Waheeda, S., Kulanthaivel, L., Subbaraj, G. K."Applications of machine learning and multimodal integration for the early diagnosis of neurodegenerative diseases (Review)". World Academy of Sciences Journal 7.6 (2025): 115.
Chicago
Velmurugan, S., Waheeda, S., Kulanthaivel, L., Subbaraj, G. K."Applications of machine learning and multimodal integration for the early diagnosis of neurodegenerative diseases (Review)". World Academy of Sciences Journal 7, no. 6 (2025): 115. https://doi.org/10.3892/wasj.2025.403
Copy and paste a formatted citation
x
Spandidos Publications style
Velmurugan S, Waheeda S, Kulanthaivel L and Subbaraj GK: Applications of machine learning and multimodal integration for the early diagnosis of neurodegenerative diseases (Review). World Acad Sci J 7: 115, 2025.
APA
Velmurugan, S., Waheeda, S., Kulanthaivel, L., & Subbaraj, G.K. (2025). Applications of machine learning and multimodal integration for the early diagnosis of neurodegenerative diseases (Review). World Academy of Sciences Journal, 7, 115. https://doi.org/10.3892/wasj.2025.403
MLA
Velmurugan, S., Waheeda, S., Kulanthaivel, L., Subbaraj, G. K."Applications of machine learning and multimodal integration for the early diagnosis of neurodegenerative diseases (Review)". World Academy of Sciences Journal 7.6 (2025): 115.
Chicago
Velmurugan, S., Waheeda, S., Kulanthaivel, L., Subbaraj, G. K."Applications of machine learning and multimodal integration for the early diagnosis of neurodegenerative diseases (Review)". World Academy of Sciences Journal 7, no. 6 (2025): 115. https://doi.org/10.3892/wasj.2025.403
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