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Alzheimer’s Disease (AD) globally represents one of the most prevalent and devastating neurodegenerative disorders. Recent advances in biomarker identification, neuroimaging, and diagnostic technologies offer promising tools to detect AD before significant symptoms appear. Artificial intelligence (AI) systems represent one of the most powerful tools for this purpose, since they are revolutionizing AD detection and management by analyzing complex patterns across multi-modal data. In this context, this paper introduces VERONECA (VErsatile and RObust approach for NEuroimaging and Clinical data-based Alzheimer’s diagnosis), a novel AI method based on an advanced multi-modal boosting approach which promotes diversity among modalities through multi-armed bandits. The method offers: i) state-of-the-art performance in AD diagnosis, through effective integration of heterogeneous data; ii) specialized learners tailored to work with specific peculiarities of each modality; and iii) a robust strategy for handling missing modalities. Our experiments show that VERONECA effectively leverages complex multi-modal data to diagnose AD. Moreover, its intrinsically modular design facilitates the seamless integration of emerging modalities as well as the adaptation to evolving clinical standards. The code of the proposed method VERONECA is publicly available at https://github.com/code-paper-veroneca/veroneca.git

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A Novel AI Approach for the Diagnosis of Alzheimer’s Disease from Multi-modal Incomplete Data

  • 会议论文

  • 发表日期：

    2025年

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  • Veronica Buttaro,
  • Giuseppe Lamanna,
  • Donato Massaro,
  • Claudio B. Caporusso,
  • Gianvito Pio,
  • Michelangelo Ceci

摘要

Alzheimer’s Disease (AD) globally represents one of the most prevalent and devastating neurodegenerative disorders. Recent advances in biomarker identification, neuroimaging, and diagnostic technologies offer promising tools to detect AD before significant symptoms appear. Artificial intelligence (AI) systems represent one of the most powerful tools for this purpose, since they are revolutionizing AD detection and management by analyzing complex patterns across multi-modal data. In this context, this paper introduces VERONECA (VErsatile and RObust approach for NEuroimaging and Clinical data-based Alzheimer’s diagnosis), a novel AI method based on an advanced multi-modal boosting approach which promotes diversity among modalities through multi-armed bandits. The method offers: i) state-of-the-art performance in AD diagnosis, through effective integration of heterogeneous data; ii) specialized learners tailored to work with specific peculiarities of each modality; and iii) a robust strategy for handling missing modalities. Our experiments show that VERONECA effectively leverages complex multi-modal data to diagnose AD. Moreover, its intrinsically modular design facilitates the seamless integration of emerging modalities as well as the adaptation to evolving clinical standards. The code of the proposed method VERONECA is publicly available at https://github.com/code-paper-veroneca/veroneca.git