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Open Access
CC BY 4.0 · Yearb Med Inform 2024; 33(01): 266-276
DOI: 10.1055/s-0044-1800756
Section 12: Sensor, Signal and Imaging Informatics
Survey

Alzheimer Disease Detection Studies: Perspective on Multi-Modal Data

Authors

  • Farzaneh Dehghani

    1   Biomedical Engineering Department, University of Calgary, Canada

  • Reihaneh Derafshi

    1   Biomedical Engineering Department, University of Calgary, Canada

  • Joanna Lin

    2   Computer Science Department, University of Calgary, Canada

  • Sayeh Bayat

    1   Biomedical Engineering Department, University of Calgary, Canada
    3   Geomatics Engineering Department, University of Calgary, Canada
    5   Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Canada

  • Mariana Bento

    1   Biomedical Engineering Department, University of Calgary, Canada
    4   Electrical and Software Engineering Department, University of Calgary, Canada
    5   Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Canada
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Summary

Objectives: Alzheimer's Disease (AD) is one of the most common neurodegenerative diseases, resulting in progressive cognitive decline, and so accurate and timely AD diagnosis is of critical importance. To this end, various medical technologies and computer-aided diagnosis (CAD), ranging from biosensors and raw signals to medical imaging, have been used to provide information about the state of AD. In this survey, we aim to provide a review on CAD systems for automated AD detection, focusing on different data types: namely, signals and sensors, medical imaging, and electronic medical records (EMR).

Methods: We explored the literature on automated AD detection from 2022-2023. Specifically, we focused on various data resources and reviewed several preprocessing and learning methodologies applied to each data type, as well as evaluation metrics for model performance evaluation. Further, we focused on challenges, future perspectives, and recommendations regarding automated AD diagnosis.

Results: Compared to other modalities, medical imaging was the most common data type. The prominent modality was Magnetic Resonance Imaging (MRI). In contrast, studies based on EMR data type were marginal because EMR is mostly used for AD prediction rather than detection. Several challenges were identified: data scarcity and bias, imbalanced datasets, missing information, anonymization, lack of standardization, and explainability.

Conclusion: Despite recent developments in automated AD detection, improving the trustworthiness and performance of these models, and combining different data types will improve usability and reliability of CAD tools for early AD detection in the clinical practice.

Keywords

Alzheimer's Disease - Signals and Sensors - Medical Imaging - Electronic Medical Records - Computer-aided Diagnosis


Publication History

Article published online:
08 April 2025

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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