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Diagnostic performance of magnetic resonance imaging–based machine learning in Alzheimer’s disease detection: a meta-analysis

  • Diagnostic Neuroradiology
  • Published:
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Abstract

Purpose

Advanced machine learning (ML) algorithms can assist rapid medical image recognition and realize automatic, efficient, noninvasive, and convenient diagnosis. We aim to further evaluate the diagnostic performance of ML to distinguish patients with probable Alzheimer’s disease (AD) from normal older adults based on structural magnetic resonance imaging (MRI).

Methods

The Medline, Embase, and Cochrane Library databases were searched for relevant literature published up until July 2021. We used the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to evaluate all included studies’ quality and potential bias. Random-effects models were used to calculate pooled sensitivity and specificity, and the Deeks’ test was used to assess publication bias.

Results

We included 24 models based on different brain features extracted by ML algorithms in 19 papers. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve for ML in detecting AD were 0.85 (95%CI 0.81–0.89), 0.88 (95%CI 0.84–0.91), 7.15 (95%CI 5.40–9.47), 0.17 (95%CI 0.12–0.22), 43.34 (95%CI 26.89–69.84), and 0.93 (95%CI 0.91–0.95).

Conclusion

ML using structural MRI data performed well in diagnosing probable AD patients and normal elderly. However, more high-quality, large-scale prospective studies are needed to further enhance the reliability and generalizability of ML for clinical applications before it can be introduced into clinical practice.

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Data Availability

Data is available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China

    Jiayi Hu, Yashan Wang, Dingjie Guo, Zihan Qu, Chuanying Sui, Guangliang He, Song Wang, Xiaofei Chen & Xin Liu

  2. School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China

    Chunpeng Wang

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Contributions

Jiayi Hu: conception and design, data analysis and interpretation, drafting and revision of the manuscript. Yashan Wang: data analysis and interpretation, drafting and revision of the manuscript. Dingjie Guo: conception and design, data analysis and interpretation. Zihan Qu: data analysis and interpretation. Chuanying Sui: data analysis and interpretation. Guangliang He: contributed to the discussion. Song Wang: contributed to the discussion. Xiaofei Chen: contributed to the discussion. Chunpeng Wang: data analysis and interpretation, revised the manuscript. Xin Liu: conception and design, revised the manuscript, approval of the final version of the manuscript. All the authors have read and approved the publication of this manuscript.

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Hu, J., Wang, Y., Guo, D. et al. Diagnostic performance of magnetic resonance imaging–based machine learning in Alzheimer’s disease detection: a meta-analysis. Neuroradiology 65, 513–527 (2023). https://doi.org/10.1007/s00234-022-03098-2

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