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An unsupervised XAI framework for dementia detection with context enrichment.

Devesh Singh1,2, Yusuf Brima3, Fedor Levin3

  • 1German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany. devesh.singh@med.uni-rostock.de.

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|November 12, 2025
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Summary
This summary is machine-generated.

Explainable AI (XAI) methods improve brain imaging analysis for dementia diagnosis. Integrating neuroanatomical features with AI relevance maps enhances transparency and clinical trust in AI decision support systems.

Keywords:
Alzheimer’s diseaseBrain volumetryExplainable artificial intelligence (XAI)Frontotemporal dementiaMagnetic resonance imagingNeurodegenerative diseasesQualitative evaluation

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Area of Science:

  • Neuroimaging and Artificial Intelligence
  • Clinical Decision Support Systems
  • Biomedical Informatics

Background:

  • Explainable Artificial Intelligence (XAI) enhances trust in AI predictions for clinical decision support.
  • Limited validation of XAI explanation quality hinders clinical adoption.
  • Convolutional Neural Networks (CNNs) are used for brain imaging analysis but require interpretable outputs.

Purpose of the Study:

  • To introduce a framework for evaluating XAI methods in dementia research by integrating neuroanatomical features with CNN relevance maps.
  • To assess the impact of different XAI explanation strategies on disease classification accuracy and clinical validity.
  • To refine XAI explanation spaces for improved transparency in AI-driven diagnostic tools.

Main Methods:

  • Trained a CNN on brain MRI scans from six cohorts (N=3253) covering normal cognition, mild cognitive impairment, Alzheimer's disease, and frontotemporal dementia.
  • Integrated neuroanatomical morphological features with CNN relevance maps for classification.
  • Implemented and evaluated three post-hoc XAI methods: model simplification, explanation-by-example, and textual explanations.
  • Conducted clustering analysis using morphological features as ground truth and qualitative clinician evaluation.

Main Results:

  • Morphology-enriched explanation spaces improved clustering homogeneity and completeness.
  • Model simplification explanations differentiated between converters and stable participants.
  • Explanation-by-example suggested cognitive trajectories, while textual explanations provided rule-based findings.
  • Clinician evaluation identified challenges and opportunities for XAI in clinical practice.

Conclusions:

  • The developed framework refines XAI explanation spaces and generation approaches for AI-based decision support in dementia research.
  • XAI methods show promise in enhancing diagnostic efficiency and clinical trust in AI systems for neurodegenerative diseases.
  • Integrating neuroanatomical features with AI relevance maps offers a robust approach to validating XAI quality.