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This study introduces a data-driven approach using graphical neural networks (GNNs) for Alzheimer's disease (AD) diagnosis, outperforming traditional clinician diagnosis. Objective clustering improves diagnostic accuracy for AD risk stratification.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Clinical diagnosis of Alzheimer's disease (AD) is inconsistent due to poor correlation between biomarkers and cognitive symptoms.
  • Existing diagnostic methods require improvement for accurate patient stratification.

Purpose of the Study:

  • To compare the performance of a graphical neural network (GNN) classifier using data-driven diagnostic classes against clinician diagnosis for Alzheimer's disease (AD).
  • To investigate the utility of unsupervised clustering on heterogeneous data for identifying objective diagnostic labels in AD.

Main Methods:

  • Unsupervised clustering was performed on tau-positron emission tomography (PET) and cognitive/functional assessment data, identifying five distinct clusters in a UMAP space.
  • A GNN classifier was trained using these data-driven clusters, with cases in one cluster re-labeled as AD based on specific characteristics.
  • The performance of the GNN trained on re-labeled data was compared to a GNN trained on clinician diagnoses.

Main Results:

  • Five clusters exhibited distinct features related to AD diagnosis, gender, family history, age, and neurological risk factors.
  • A GNN trained on re-labeled AD cases achieved a higher multiclass area-under-the-curve (AUC) of 95.2% compared to 91.7% for a GNN trained on clinician diagnoses (p=0.02).
  • Re-labeled AD cases showed high cerebrospinal fluid amyloid beta (CSF Aβ) levels at a younger age, despite Aβ data not being used in clustering.

Conclusions:

  • Objective, cluster-based diagnostic labels derived from unsupervised learning can enhance AD classification accuracy.
  • GNNs combined with data-driven labels show promise for improving clinical risk stratification and diagnosis of Alzheimer's disease.
  • This approach offers a more objective method for diagnosing AD, potentially leading to more consistent clinical outcomes.