Fusion of deep learning models of MRI scans, Mini–Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment. Issue 1 (27th September 2018)

• Record Type: Journal Article
• Authors: Qiu, Shangran; Chang, Gary H.; Panagia, Marcello; Gopal, Deepa M.; Au, Rhoda; Kolachalama, Vijaya B.
• Is Part Of: Alzheimer's & dementia. Volume 10:Issue 1(2018)
• Page Start: 737
• Page End: 749
• Publication Date: 2018-09-27
• Subjects: MRI -- MMSE -- LM test -- Mild cognitive impairment -- Deep learning -- Convolutional neural network -- Multilayer perceptron -- Majority voting; Alzheimer's disease -- Periodicals; Alzheimer's disease -- Diagnosis -- Periodicals; Dementia -- Periodicals; Dementia -- Diagnosis -- Periodicals; 616.831
• DOI: 10.1016/j.dadm.2018.08.013
• Languages: English
• ISSNs: 2352-8729

Abstract

Abstract: Introduction: Our aim was to investigate if the accuracy of diagnosing mild cognitive impairment (MCI) using the Mini–Mental State Examination (MMSE) and logical memory (LM) test could be enhanced by adding MRI data. Methods: Data of individuals with normal cognition and MCI were obtained from the National Alzheimer Coordinating Center database (n = 386). Deep learning models trained on MRI slices were combined to generate a fused MRI model using different voting techniques to predict normal cognition versus MCI. Two multilayer perceptron (MLP) models were developed with MMSE and LM test results. Finally, the fused MRI model and the MLP models were combined using majority voting. Results: The fusion model was superior to the individual models alone and achieved an overall accuracy of 90.9%. Discussion: This study is a proof of principle that multimodal fusion of models developed using MRI scans, MMSE, and LM test data is feasible and can better predict MCI.