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Cited by 73Dementia and Cognitive Impairment ResearchArtificial Intelligence in HealthcareAlzheimer's disease research and treatments

A Multi-modal Convolutional Neural Network Framework for the Prediction of Alzheimer’s Disease

Spasov, Simeon E., Passamonti, Luca, Duggento, Andrea, Lio, Pietro, Toschi, Nicola·2018·2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DOI 10.1109/embc.2018.8512468
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Abstract

This paper presents a multi-modal Alzheimer's disease (AD) classification framework based on a convolutional neural network (CNN) architecture. The devised model takes structural MRI, and clinical assessment and genetic (APOe4) measures as inputs. Our CNN structure is designed to be efficient in its use of parameters which reduces overfitting, computational complexity, memory requirements and speed of prototyping. This is achieved by factorising the convolutional layers in parallel streams which also enables the simultaneous extraction of high and low level feature representations. Our method consistently achieves high classification results in discriminating between AD and control subjects with an average of 99% accuracy, 98% sensitivity, 100% specificity and an AUC of 1 across all test folds. Our study confirms that careful tuning of CNN characteristics can result in a framework which delivers extremely accurate predictions in a clinical problem despite data paucity, opening new avenues for application to prediction tasks which regard patient stratification, prediction of clinical evolution and eventually personalised medicine applications.

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Crossref · 10.1109/embc.2018.8512468

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14 of 14 references verified0 citation errors · 0 not checkable

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  • Prediction of Mild Cognitive Impairment or Alzheimer's disease based on ADNI dataset is frequently investigated, but predictive accuracy varies, and many studies do not discuss which input modalities or features contributed most to prediction.

    “For example, the prediction of Mild Cognitive Impairment or Alzheimer’s disease based on the ADNI dataset is frequently investigated27–32. But their predictive accuracy varies, and many studies do not discuss which input modalities or features contributed most to the prediction.”

    From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [31]

  • Spasov et al. and Yala et al. both used simple concatenation to fuse the learned imaging and clinical features.

    “Spasov et al. and Yala. et al. both used simple concatenation to fuse the learned imaging and clinical features.”

    From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [36]

  • Spasov et al., Yala et al., and Yoo et al. implemented CNNs to learn image features and fused these feature representations with clinical features before feeding them into a feed-forward neural network.

    “Spasov et al.36, Yala et al.37, and Yoo et al.38 implemented CNNs to learn image features and fused these feature representations with clinical features before feeding them into a feed-forward neural network.”

    From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [36]

  • Fusion strategies were often applied to the diagnosis and prediction of Alzheimer's disease.

    “For example, fusion strategies were often applied to the diagnosis and prediction of Alzheimer’s disease25,28,33,36,41.”

    From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [36]

  • Various combinations of imaging, clinical, and genomic modalities can be fused for tasks such as AD prediction, AD stage detection, and liver transplantation risk assessment.

    “For instance, MRI and clinical data can be fused for AD prediction [72] or MRI, clinical and genomic modalities can be fused for AD stage detection [69]. Also, latent representations of multiple imaging modalities and clinical data can be fused for risk assessment of liver transplantation for hepatocellular cancer [71].”

    From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [72]

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Authors
Spasov, S. E., Passamonti, L., Duggento, A., Lio, P., & Toschi, N.
Affiliations
  • University of Cambridge (GB)
  • University of Rome Tor Vergata (IT)
ORCID
Venue
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher
IEEE
Published
2018-07
Volume
2018
Pages
1271-1274
ISSN
2375-7477 (ISSN-L)
DOI
10.1109/embc.2018.8512468
OpenAlex
W2898709101
Cited by
73
References
16
Citations by year
2026: 3 · 2025: 6 · 2024: 17 · 2023: 19 · 2022: 11 · 2021: 5 · 2020: 8 · 2019: 4
FWCI
13.24
Type
conference paper
Language
en
Open access
closed
Locations
  • Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
  • PubMed (publishedVersion)
  • Cineca Institutional Research Information System (Tor Vergata University) (submittedVersion)
Funders
Engineering and Physical Sciences Research Council (1620072)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Keywords
Alzheimer's disease prediction, multimodal classification, convolutional neural networks, structural MRI, APOE ε4, clinical evaluation, feature extraction, data scarcity, patient stratification, personalized medicine
MeSH terms
Alzheimer Disease, Humans, Magnetic Resonance Imaging, Nerve Net, Neural Networks, Computer

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