BACKGROUND
Mild cognitive impairment is a transitional stage between normal cognitive aging and dementia. Identifying individuals at the preMCI stage, prior to the onset of mild cognitive decline, can be pivotal for early interventions aimed to reduce the progression neurodegeneration.
OBJECTIVE
The objective of this study is to develop convolutional neural networks trained on fluid attenuated inversion recovery magnetic resonance imaging for classification of preMCI in an imbalanced cohort of 350 participants.
METHODS
A DenseNet 264 convolutional neural network was trained on an imbalanced dataset of 350 participants with a dataset split into 64%, training, 16% validation, and 20% testing sets. Training was conducted with a batch size=70, epoch=200, processing images resized to a uniform dimension of 128×128×128 voxels, and optimizer=Adam. The optimization of our network was conducted using the Adam Optimizer with a learning rate of 10-3 and a weight decay of 5-4. Data augmentation strategy included Random affine transformation, random rotation across an axis, and random Gaussian noise on each image during training.
RESULTS
Mean age of the participants was 71.6 (SD 5.14); the average educational attainment was 16.3 years (SD 2.39). The mean MoCA score was 26.7 (SD 1.93). Our DenseNet mode achieved R2=0.146 and RMSE=0.569.
CONCLUSIONS
These findings underscore the potential of brain imaging and DL to identify this at-risk population, offering a promising tool for early detection and potential personalized preventative strategies against cognitive decline. Further research is warranted to improve upon the results to validate these findings in a larger, more diverse population.