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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Improving Alzheimer Diagnoses With An Interpretable Deep Learning Framework: Including Neuropsychiatric Symptoms.

Shujuan Liu1, Yuanjie Zheng1, Hongzhuang Li1

  • 1School of Information Science and Engineering, Shandong Normal University, Shandong, China.

Neuroscience
|September 14, 2023
PubMed
Summary

This study introduces a hybrid deep learning model for Alzheimer's disease (AD) diagnosis using MRI and clinical data. The model accurately identifies AD, highlighting apathy as a key indicator.

Keywords:
3D CNNAlzheimer’s diseasedeep learningneuropsychiatric symptomsstructural MRI

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Alzheimer's disease (AD) is a progressive neurodegenerative disorder.
  • Neuropsychiatric symptoms (NPS) are common in AD and linked to its severity.
  • Current research methods for linking NPS and AD severity lack intuitive approaches.

Purpose of the Study:

  • To develop a hybrid deep learning framework for accurate Alzheimer's disease diagnosis.
  • To integrate multimodal data including neuroimaging and clinical information.
  • To investigate the role of specific neuropsychiatric symptoms in AD diagnosis.

Main Methods:

  • A hybrid deep learning framework combining a 3D convolutional neural network (CNN) and Principal Component Analysis (PCA).
  • Utilized multimodal inputs: structural MRI, behavioral scores, age, and gender.
  • Employed SHapley Additive exPlanations (SHAP) for interpreting model decisions.

Main Results:

  • Achieved high accuracy (0.91) and Area Under the Curve (0.97) in classifying AD from cognitively normal individuals.
  • Demonstrated the model's effectiveness in integrating imaging and non-imaging data.
  • Identified apathy as a significant neuropsychiatric symptom contributing to AD diagnosis.

Conclusions:

  • The proposed hybrid deep learning framework offers a robust method for Alzheimer's disease diagnosis.
  • Apathy emerges as a crucial factor for consideration in AD diagnosis, clinical trials, and future research.
  • Multimodal data integration significantly enhances diagnostic accuracy for Alzheimer's disease.