Abstract
PurposeThe purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer’s disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model’s performance to that of multiple expert nuclear medicine physicians’ readers.Materials and methodsRetrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer’s disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model’s performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention.ResultsThe proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6–100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7–100) in AD, 71.4% (51.6–91.2) in MCI-AD, and 94.7% (90–99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders.ConclusionUsing only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.
Highlights
Neurodegenerative dementias have a huge negative impact on the healthcare systems globally, especially with increasing older population
Mild cognitive impairment (MCI) is a prodromal form of dementia, defined by cognitive impairment not interfering with activities of daily living, leading to Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), or other degenerative dementias [5, 6]
This study introduces a 3D-CNN model that can predict the final clinical diagnosis of cognitively normal (CN), MCI due to AD and patterns of some types of dementia which can represent a challenge in their differentiation for the average reader, like AD and DLB
Summary
Neurodegenerative dementias have a huge negative impact on the healthcare systems globally, especially with increasing older population. Dementia with Lewy bodies (DLB) is another common neurodegenerative disorder, accounting for up to 30% of all cases of dementia [3] and is often misdiagnosed and unrecognized [4]. Mild cognitive impairment (MCI) is a prodromal form of dementia, defined by cognitive impairment not interfering with activities of daily living, leading to AD, DLB, or other degenerative dementias [5, 6]. The diagnosis of such disorders is challenging, even for experienced neurologists, making the decision of the use of the appropriate treatment difficult in some cases. Physicians use diagnostic tests such as neurofunctional imaging in order to provide more accurate clinical assessments [7]. 18F-FDG PET scans, which measure cerebral glucose metabolism, have been reported as a useful biomarker for the discrimination of the above-mentioned neurodegenerative disorders [8]
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