Deep learning for FDG-PET classification in patients with Alzheimer's disease, dementia with Lewy bodies and their mixed pathology: a solution for diagnostic heterogeneity.

INTRODUCTION

Mixed pathology of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are frequently observed in patients with cognitive impairment, and complicate clinical diagnosis. We aimed to develop a classification model using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) to improve diagnostic accuracy for these challenging cases.

METHODS

We analyzed FDG-PET images from 277 participants who were categorized into AD, DLB, mixed disease, and healthy control (HC) groups. Deep learning-based classification models were trained on seven binary classification tasks and one multiclass classification task and subsequently integrated into an ensemble model to predict AD, DLB, mixed disease or HC groups.

RESULTS

The model achieved an AUROC of 0.73 (95% CI, 0.69-0.78) for AD, 0.90 (95% CI, 0.89-0.91) for DLB, 0.71 (95% CI, 0.66-0.75) for Mixed, and 0.87 (95% CI, 0.84-0.89) for HC.

DISCUSSION: The model represents the state-of-the-art in automatic FDG-PET-based classification of AD, DLB, Mixed, and HC. This study highlights the utility of FDG-PET as a biomarker for differentiating AD, DLB, Mixed, and HC groups, resolving diagnostic challenges caused by overlapping clinical features.