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Alzheimer's Disease: Overview

Alzheimer's Disease: Overview

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.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...

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Updated: May 15, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Published on: December 15, 2023

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 summary is machine-generated.

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.

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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.