Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain

Junwei Duan; Yang Liu; Huanhua Wu; Jing Wang; Long Chen; C L Philip Chen

doi:10.3389/fnins.2023.1137567

Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain

Front Neurosci. 2023 Mar 13:17:1137567. doi: 10.3389/fnins.2023.1137567. eCollection 2023.

Authors

Junwei Duan^{1

2}, Yang Liu¹, Huanhua Wu³, Jing Wang⁴, Long Chen⁵, C L Philip Chen⁶

Affiliations

¹ College of Information Science and Technology, Jinan University, Guangzhou, China.
² Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Jinan University, Guangzhou, China.
³ Department of Nuclear Medicine and PET/CT-MRI Centre, The First Affiliated Hospital of Jinan University, Guangzhou, China.
⁴ School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China.
⁵ Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.
⁶ School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disease, and the development of AD is irreversible. However, preventive measures in the presymptomatic stage of AD can effectively slow down deterioration. Fluorodeoxyglucose positron emission tomography (FDG-PET) can detect the metabolism of glucose in patients' brains, which can help to identify changes related to AD before brain damage occurs. Machine learning is useful for early diagnosis of patients with AD using FDG-PET, but it requires a sufficiently large dataset, and it is easy for overfitting to occur in small datasets. Previous studies using machine learning for early diagnosis with FDG-PET have either involved the extraction of elaborately handcrafted features or validation on a small dataset, and few studies have explored the refined classification of early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI). This article presents a broad network-based model for early diagnosis of AD (BLADNet) through PET imaging of the brain; this method employs a novel broad neural network to enhance the features of FDG-PET extracted via 2D CNN. BLADNet can search for information over a broad space through the addition of new BLS blocks without retraining of the whole network, thus improving the accuracy of AD classification. Experiments conducted on a dataset containing 2,298 FDG-PET images of 1,045 subjects from the ADNI database demonstrate that our methods are superior to those used in previous studies on early diagnosis of AD with FDG-PET. In particular, our methods achieved state-of-the-art results in EMCI and LMCI classification with FDG-PET.

Keywords: Alzheimer's disease; PET; broad learning system; computer-aided diagnosis; neural network.

Grants and funding

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. This work was supported in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515011999, in part by the National Key Research and Development Program of China under Grant 2018YFC2002500, and in part by Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization under Grant 2021B1212040007.