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Alzheimer's Disease: Overview01:26

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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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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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A Cross-Modal Mutual Knowledge Distillation Framework for Alzheimer's Disease Diagnosis: Addressing Incomplete

Min Gu Kwak1, Lingchao Mao1, Zhiyang Zheng1

  • 1H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

IEEE Transactions on Automation Science and Engineering : a Publication of the IEEE Robotics and Automation Society
|September 2, 2025
PubMed
Summary
This summary is machine-generated.

Early Alzheimer's Disease (AD) detection is improved using a novel deep learning framework. This method effectively handles incomplete neuroimaging data, enhancing diagnostic accuracy for AD patients.

Keywords:
Alzheimer’s diseaseincomplete multimodal datasetsknowledge distillationmild cognitive impairmentrepresentation disentanglement

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Area of Science:

  • Artificial Intelligence
  • Neuroimaging
  • Medical Diagnostics

Background:

  • Early detection of Alzheimer's Disease (AD) is critical for effective treatment and patient outcomes.
  • Multimodal neuroimaging data integration can improve AD detection.
  • Real-world data often presents challenges with incomplete modalities due to cost and accessibility.

Purpose of the Study:

  • To propose a deep learning framework, Incomplete Cross-modal Mutual Knowledge Distillation (IC-MKD), to address incomplete multimodal neuroimaging data for early AD detection.
  • To develop a model that can effectively learn from patients with varying available imaging modalities.
  • To enhance the performance of both multimodal and single-modality models in AD diagnosis.

Main Methods:

  • Developed a deep learning framework (IC-MKD) utilizing a teacher-student model approach.
  • Implemented a Modality-Disentangling Teacher (MDT) model using information disentanglement.
  • Designed a student model learning from classification errors and teacher knowledge, with the teacher enhanced by student's single-modal features.
  • Validated the method through theoretical analysis, simulation studies, and a case study using Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets.

Main Results:

  • The proposed IC-MKD framework effectively models sub-cohorts based on available modalities.
  • Demonstrated the framework's ability to handle incomplete multimodal neuroimaging data.
  • The method shows potential for advancing early AD detection using AI.

Conclusions:

  • The IC-MKD framework offers a robust solution for leveraging incomplete multimodal neuroimaging data in AI-driven AD detection.
  • This approach has significant implications for improving diagnostic accuracy and timely intervention in Alzheimer's Disease.
  • Artificial intelligence holds great promise for overcoming data limitations in clinical neuroimaging research.