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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
Published on: December 15, 2023
916
Alzheimer Disease Detection Studies: Perspective on Multi-Modal Data
Farzaneh Dehghani1, Reihaneh Derafshi1, Joanna Lin2
1Biomedical Engineering Department, University of Calgary, Canada.
Yearbook of Medical Informatics
|April 8, 2025
Summary
This review explores computer-aided diagnosis (CAD) for Alzheimer's Disease (AD) detection using signals, imaging, and electronic medical records (EMR). Medical imaging, particularly MRI, is most common, but challenges like data scarcity hinder reliable early AD diagnosis.
Area of Science:
- Neurodegenerative disease research
- Medical imaging analysis
- Artificial intelligence in healthcare
Background:
- Alzheimer's Disease (AD) is a progressive neurodegenerative disorder.
- Accurate and timely diagnosis of AD is crucial for patient management.
- Computer-aided diagnosis (CAD) systems offer potential for automated AD detection.
Purpose of the Study:
- To review CAD systems for automated Alzheimer's Disease detection.
- To focus on diverse data types: signals, sensors, medical imaging, and electronic medical records (EMR).
- To identify challenges and future directions in automated AD diagnosis.
Main Methods:
- Literature review of automated AD detection from 2022-2023.
- Analysis of data resources, preprocessing, and learning methodologies.
- Evaluation of model performance metrics and identification of challenges.
Main Results:
- Medical imaging, especially MRI, is the most prevalent data type for AD detection.
- Electronic Medical Records (EMR) are more utilized for AD prediction than detection.
- Key challenges include data scarcity, bias, imbalanced datasets, and lack of explainability.
Conclusions:
- Advancements in automated AD detection are ongoing.
- Improving model trustworthiness, performance, and data integration is essential.
- Enhanced CAD tools are vital for reliable early AD detection in clinical practice.
