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Dual attention based fusion network for MCI Conversion Prediction.

Min Luo1, Zhen He1, Hui Cui1

  • 1Department of Computer Science and Information Technology, La Trobe University, Melbourne Vic, 3086, Australia.

Computers in Biology and Medicine
|September 4, 2024
PubMed
Summary

This study introduces a dual attention network for predicting Alzheimer's disease progression in mild cognitive impairment (MCI) patients. The model accurately distinguishes between progressive and stable MCI using MRI and neurocognitive data.

Keywords:
Alzheimer’s diseaseAttention mechanismMild cognitive impairmentPredictionSelf attention encoder

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Alzheimer's disease (AD) poses a significant global health challenge.
  • Early prediction of disease progression in mild cognitive impairment (MCI) is crucial for effective interventions and clinical research.

Purpose of the Study:

  • To develop a novel dual attention network for classifying progressive MCI (pMCI) and stable MCI (sMCI).
  • To leverage both magnetic resonance imaging (MRI) and neurocognitive metadata for enhanced classification accuracy.

Main Methods:

  • Utilized a 3D CNN ShuffleNet V2 model for extracting features from MRI scans.
  • Employed neurocognitive metadata to guide a spatial attention mechanism for focusing on discriminative brain regions.
  • Implemented a Vision Transformer (ViT) based self-attention mechanism for fusing MRI features and neurocognitive data.

Main Results:

  • Achieved an accuracy of 81.34%, AUC of 0.874, and sensitivity of 0.85 via 5-fold cross-validation.
  • Demonstrated superior performance compared to existing methods for MCI progression classification.
  • Ablation studies confirmed the significant contribution of both fusion methods to the model's performance.

Conclusions:

  • The proposed dual attention network offers a promising approach for accurate MCI progression prediction.
  • Integrating multimodal data (MRI and neurocognitive metadata) with advanced deep learning techniques enhances classification capabilities.
  • This method holds potential for improving patient management and accelerating Alzheimer's disease research.