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Feature Integration of [18F]FDG PET Brain Imaging Using Deep Learning for Sensitive Cognitive Decline Detection.

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This study introduces a novel deep learning framework combining PET imaging and regional data to improve the diagnosis of cognitive decline. The integrated model achieved higher accuracy than single-feature approaches, enhancing early detection and intervention for conditions like Alzheimer's disease.

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Medical Diagnostics

Background:

  • Accurate diagnosis of cognitive decline (CD), including early Alzheimer's disease, is crucial for timely intervention.
  • Positron emission tomography (PET) reveals functional brain changes in CD but faces limitations due to cost and radiation.
  • A data-efficient multi-representational learning framework is proposed to enhance PET's clinical utility.

Purpose of the Study:

  • To develop and validate a novel deep learning framework for distinguishing cognitively normal (CN) individuals from those with cognitive decline (CD).
  • To leverage both voxel-level imaging data and region-level quantification for improved diagnostic accuracy.
  • To enhance the data efficiency and clinical applicability of PET in diagnosing CD.

Main Methods:

  • Extracted voxel-level features from [¹⁸F]FDG PET using CNNs and PCANets.
  • Derived region-level features using DNNs from standardized uptake value ratio measurements.
  • Integrated voxel- and region-level features via direct concatenation and applied various machine learning models for prediction, validated on 252 ADNI participants.

Main Results:

  • The integrated DNN-CNN model achieved the highest classification accuracy (0.87 ± 0.05), outperforming individual models.
  • This fusion approach demonstrated a 6.10% accuracy improvement, increased Recall by 14.29%, and F1-Score by 7.32% compared to the DNN-only model.
  • Model performance showed significant correlation with MMSE scores and surpassed MMSE-based classification in accuracy, recall, and F1-score.

Conclusions:

  • Combining PET imaging, region-level quantification, and deep learning significantly improves diagnostic performance for cognitive decline.
  • Fusion-based deep learning strategies enhance sensitivity in detecting cognitive decline, offering a more accurate and data-efficient approach.
  • This multimodal strategy supports broader clinical application of PET for diagnosing cognitive decline and related conditions like Alzheimer's disease.