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Development and validation of an automatic classification algorithm for the diagnosis of Alzheimer’s disease using a high-performance interpretable deep learning network

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Abstract

Objectives

To develop and validate an automatic classification algorithm for diagnosing Alzheimer’s disease (AD) or mild cognitive impairment (MCI).

Methods and materials

This study evaluated a high-performance interpretable network algorithm (TabNet) and compared its performance with that of XGBoost, a widely used classifier. Brain segmentation was performed using a commercially approved software. TabNet and XGBoost were trained on the volumes or radiomics features of 102 segmented regions for classifying subjects into AD, MCI, or cognitively normal (CN) groups. The diagnostic performances of the two algorithms were compared using areas under the curves (AUCs). Additionally, 20 deep learning–based AD signature areas were investigated.

Results

Between December 2014 and March 2017, 161 AD, 153 MCI, and 306 CN cases were enrolled. Another 120 AD, 90 MCI, and 141 CN cases were included for the internal validation. Public datasets were used for external validation. TabNet with volume features had an AUC of 0.951 (95% confidence interval [CI], 0.947–0.955) for AD vs CN, which was similar to that of XGBoost (0.953 [95% CI, 0.951–0.955], p = 0.41). External validation revealed the similar performances of two classifiers using volume features (0.871 vs. 0.871, p = 0.86). Likewise, two algorithms showed similar performances with one another in classifying MCI. The addition of radiomics data did not improve the performance of TabNet. TabNet and XGBoost focused on the same 13/20 regions of interest, including the hippocampus, inferior lateral ventricle, and entorhinal cortex.

Conclusions

TabNet shows high performance in AD classification and detailed interpretation of the selected regions.

Clinical relevance statement

Using a high-performance interpretable deep learning network, the automatic classification algorithm assisted in accurate Alzheimer’s disease detection using 3D T1-weighted brain MRI and detailed interpretation of the selected regions.

Key Points

• MR volumetry data revealed that TabNet had a high diagnostic performance in differentiating Alzheimer’s disease (AD) from cognitive normal cases, which was comparable with that of XGBoost.

• The addition of radiomics data to the volume data did not improve the diagnostic performance of TabNet.

• Both TabNet and XGBoost selected the clinically meaningful regions of interest in AD, including the hippocampus, inferior lateral ventricle, and entorhinal cortex.

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Abbreviations

AD:

Alzheimer’s disease

ADNI:

Alzheimer Disease Neuroimaging Initiative

AIBL:

Australian Imaging Biomarkers and Lifestyle Study of Aging

AUC:

Area under the curve

CDR:

Clinical dementia rating

CN:

Cognitive normal

MCI:

Mild cognitive impairment

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Funding

This work was supported by the National Research Foundation of Korea (NRF-2021R1C1C1014413 to Chong Hyun Suh).

Author information

Author notes

  1. Ho Young Park and Woo Hyun Shim contributed equally.

Authors and Affiliations

  1. Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Ho Young Park, Chong Hyun Suh, Ho Sung Kim & Sang Joon Kim

  2. Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Woo Hyun Shim & Hwon Heo

  3. VUNO Inc., Seoul, Republic of Korea

    Hyun Woo Oh, Jinyoung Kim & Jinkyeong Sung

  4. Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Jae-Sung Lim & Jae-Hong Lee

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  1. Ho Young Park
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Corresponding author

Correspondence to Chong Hyun Suh.

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Guarantor

The scientific guarantor of this publication is Chong Hyun Suh, M.D.

Conflict of interest

Hyun Woo Oh, M.S.; Kim Jingyoung, M.S.; and Jinkyeong Sung, M.D., Ph.D. are employees of VUNO Inc. The authors report no other conflicts of interest, and the present study has not been presented elsewhere.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was not required due to the retrospective nature of the study.

Ethical approval

This study was approved by the institutional review board of Asan Medical Center.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Park, H.Y., Shim, W.H., Suh, C.H. et al. Development and validation of an automatic classification algorithm for the diagnosis of Alzheimer’s disease using a high-performance interpretable deep learning network. Eur Radiol 33, 7992–8001 (2023). https://doi.org/10.1007/s00330-023-09708-8

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