# Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging

arXiv: https://arxiv.org/abs/1704.06033 · HTML: https://mlsanity.com/paper/1704.06033/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2017.

## Built on by (most cited first, 9 of 9)

- [Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Synthesized Functional MRI Data](https://mlsanity.com/paper/2104.04672/) (2021)
- [Vision Transformers and Bi-LSTM for Alzheimer's Disease Diagnosis from 3D MRI](https://mlsanity.com/paper/2401.03132/) (2024)
- [Deep Insights into Cognitive Decline: A Survey of Leveraging Non-Intrusive Modalities with Deep Learning Techniques](https://mlsanity.com/paper/2410.18972/) (2024)
- [A Novel Solution of an Elastic Net Regularization for Dementia Knowledge Discovery using Deep Learning](https://mlsanity.com/paper/2109.00896/) (2021)
- [Bilinear pooling and metric learning network for early Alzheimer's disease identification with FDG-PET images](https://mlsanity.com/paper/2111.04985/) (2021)
- [AI and Non AI Assessments for Dementia](https://mlsanity.com/paper/2307.01210/) (2023)
- [Perivascular space Identification Nnunet for Generalised Usage (PINGU)](https://mlsanity.com/paper/2405.08337/) (2024)
- [Explainable Graph-theoretical Machine Learning with Application to Alzheimer's Disease Prediction](https://mlsanity.com/paper/2503.16286/) (2025)
- [Emergence of Transfer Learning towards Specific Identification of Alzheimer's Disease A Prospective Approach](https://mlsanity.com/paper/2608.14731/) (2026)

## Abstract (arXiv)

For effective treatment of Alzheimer disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. Herein, we developed a novel framework based on a deep convolutional neural network which can predict future cognitive decline in mild cognitive impairment (MCI) patients using flurodeoxyglucose and florbetapir positron emission tomography (PET). The architecture of the network only relies on baseline PET studies of AD and normal subjects as the training dataset. Feature extraction and complicated image preprocessing including nonlinear warping are unnecessary for our approach. Accuracy of prediction (84.2%) for conversion to AD in MCI patients outperformed conventional feature-based quantification approaches. ROC analyses revealed that performance of CNN-based approach was significantly higher than that of the conventional quantification methods (p < 0.05). Output scores of the network were strongly correlated with the longitudinal change in cognitive measurements. These results show the feasibility of deep learning as a tool for predicting disease outcome using brain images.