Exploring the Value of MRI Measurement of Hippocampal Volume for Predicting the Occurrence and Progression of Alzheimer’s Disease Based on Artificial Intelligence Deep Learning Technology and Evidence-Based Medicine Meta-Analysis

Article type: Research Article

Authors: Zhou, Jianguo^{a; 1} | Zhao, Mingli^{b; 1} | Yang, Zhou^c | Chen, Liping^c | Liu, Xiaoli^{c; *} | for the Alzheimer’s Disease Neuroimaging Initiative²

Affiliations: [a] Department of Radiology, Lianyungang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Lianyungang, China | [b] Department of Radiology, The Fourth People’s Hospital of Lianyungang Affiliated to Nanjing Medical University Kangda, Lianyungang, China | [c] Department of Rehabilitation, Lianyungang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Lianyungang, China

Correspondence: [*] Correspondence to: Xiaoli Liu, Department of Rehabilitation, Lianyungang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, No.160, Chaoyang Middle Road, Haizhou District, Lianyungang 222004, Jiangsu Province, P. R. China. Tel.: +86 13655137087. E-mail: 13655137087@163.com.

Note: [1] These authors contributed equally to this work.

Note: [2] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: Background:Alzheimer’s disease (AD), a major dementia cause, lacks effective treatment. MRI-based hippocampal volume measurement using artificial intelligence offers new insights into early diagnosis and intervention in AD progression. Objective:This study, involving 483 AD patients, 756 patients with mild cognitive impairment (MCI), and 968 normal controls (NC), investigated the predictive capability of MRI-based hippocampus volume measurements for AD risk using artificial intelligence and evidence-based medicine. Methods:Utilizing data from ADNI and OASIS-brains databases, three convolutional neural networks (InceptionResNetv2, Densenet169, and SEResNet50) were employed for automated AD classification based on structural MRI imaging. A multitask deep learning model and a densely connected 3D convolutional network were utilized. Additionally, a systematic meta-analysis explored the value of MRI-based hippocampal volume measurement in predicting AD occurrence and progression, drawing on 23 eligible articles from PubMed and Embase databases. Results:InceptionResNetv2 outperformed other networks, achieving 99.75% accuracy and 100% AUC for AD-NC classification and 99.16% accuracy and 100% AUC for MCI-NC classification. Notably, at a 512×512 size, InceptionResNetv2 demonstrated a classification accuracy of 94.29% and an AUC of 98% for AD-NC and 97.31% accuracy and 98% AUC for MCI-NC. Conclusions:The study concludes that MRI-based hippocampal volume changes effectively predict AD onset and progression, facilitating early intervention and prevention.

Keywords: Alzheimer’s disease, artificial intelligence, deep learning, evidence-based medicine, hippocampal volume, magnetic resonance imaging

DOI: 10.3233/JAD-230733

Journal: Journal of Alzheimer's Disease, vol. 97, no. 3, pp. 1275-1288, 2024