Deep Learning With 18F-Fluorodeoxyglucose-PET Gives Valid Diagnoses for the Uncertain Cases in Memory Impairment of Alzheimer’s Disease

• Published in: Frontiers in aging neuroscience Vol. 13; p. 764272
• Main Authors: Zhang, Wei, Zhang, Tianhao, Pan, Tingting, Zhao, Shilun, Nie, Binbin, Liu, Hua, Shan, Baoci
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 15.12.2021; Frontiers Media S.A
• Subjects: Alzheimer's disease; Classification; Cognitive ability; Deep learning; Dementia; Dementia disorders; Diagnosis; FDG-PET; Medical imaging; Memory; Methods; Neural networks; Neurodegeneration; Neurodegenerative diseases; Neuroimaging; neuropsychologial assessment; Neuropsychology; Neuroscience; Positron emission tomography; Standard deviation; uncertain; FDG-PET; deep learning; neuropsychologial assessment; Alzheimer’s disease; uncertain
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2021.764272

Objectives: Neuropsychological tests are an important basis for the memory impairment diagnosis in Alzheimer’s disease (AD). However, multiple memory tests might be conflicting within-subjects and lead to uncertain diagnoses in some cases. This study proposed a framework to diagnose the uncertain cases of memory impairment. Methods: We collected 2,386 samples including AD, mild cognitive impairment (MCI), and cognitive normal (CN) using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and three different neuropsychological tests (Mini-Mental State Examination, Alzheimer’s Disease Assessment Scale-Cognitive Subscale, and Clinical Dementia Rating) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A deep learning (DL) framework using FDG-PET was proposed to diagnose uncertain memory impairment cases that were conflicting between tests. Subsequent ANOVA, chi-squared, and t -test were used to explain the potential causes of uncertain cases. Results: For certain cases in the testing set, the proposed DL framework outperformed other methods with 95.65% accuracy. For the uncertain cases, its positive diagnoses had a significant ( p < 0.001) worse decline in memory function than negative diagnoses in a longitudinal study of 40 months on average. In the memory-impaired group, uncertain cases were mainly explained by an AD metabolism pattern but mild in extent ( p < 0.05). In the healthy group, uncertain cases were mainly explained by a non-energetic mental state ( p < 0.001) measured using a global deterioration scale (GDS), with a significant depression-related metabolism pattern detected ( p < 0.05). Conclusion: A DL framework for diagnosing uncertain cases of memory impairment is proposed. Proved by longitudinal tracing of its diagnoses, it showed clinical validity and had application potential. Its valid diagnoses also provided evidence and explanation of uncertain cases based on the neurodegeneration and depression mental state.