Brain Age Prediction Using 3D Convolutional Neural Networks: An End-to-End Deep Learning Solution from MRI Images to Precision Medicine

This article introduces an open-source Python project that uses the 3D ResNet-18 deep learning model to predict brain age from T1-weighted MRI scans. The project implements an end-to-end training pipeline, including a MONAI preprocessing pipeline, mixed-precision training, Huber loss function, and bias correction mechanism, providing a reliable technical foundation for neurodegenerative disease research and healthy aging assessment. The project supports two modeling approaches (the recommended 3D ResNet-18 scheme and the legacy VGG16+SVR scheme) and offers a Streamlit demo application and cloud training support.

Brain aging is not synchronized with chronological age; brain age is an indicator reflecting the biological state of the brain—brain age older than chronological age may indicate a risk of neurodegenerative diseases, while the opposite may mean better cognitive reserve. The development of deep learning technology has made it possible to automatically extract features from medical images for modeling. This project uses 3D CNN to directly process MRI volume data for high-precision prediction.

The project provides two modeling approaches:

1. 3D ResNet-18 (Recommended)：An end-to-end 3D convolutional network that automatically captures 3D structural features of the brain without manual feature extraction. The architecture includes a Stem layer (7×7×7 convolution), four residual stages, global average pooling, and a fully connected layer to output brain age.
2. VGG16+SVR (Legacy)：A transfer learning approach that uses pre-trained VGG16 to extract features from MRI slices and predicts age via SVR, supporting early/late fusion strategies.

Preprocessing：Using the MONAI framework, the process includes RAS reorientation, isotropic resampling (2mm resolution), size standardization (96×96×96), Z-score normalization, and automated data cleaning (detecting corrupted files, abnormal data, etc.). Training Strategy：

• Loss function: Huber loss (δ=1.0), robust to outliers;
• Optimizer: AdamW (weight decay 1e-4), with learning rate warm-up (linear increase in the first 5 epochs) + cosine annealing;
• Mixed-precision training (AMP) to accelerate training;
• Data split: 70%/15%/15% for training/validation/testing, cyclically allocated after sorting by age to ensure coverage of the full age range.

Post-Processing：A linear regression model is used to correct the regression to the mean phenomenon (bias where predictions of extreme values tend to move toward the mean). Evaluation Metrics：MAE (Mean Absolute Error), RMSE (Root Mean Squared Error), Pearson correlation coefficient. Results：The 3D ResNet-18 scheme achieves an MAE of approximately 4-5 years on the test set and a Pearson correlation coefficient of 0.90-0.95, which is significantly better than VGG16+SVR (MAE of approximately 7-9 years).

The project provides:

1. Streamlit Interactive Web Application：Users can upload trained model checkpoints and MRI files (.nii/.nii.gz) to get real-time brain age prediction results, making it easy for non-programming users to use;
2. Cloud Training Support：Google Colab/Kaggle Notebook versions with pre-configured GPU environments. Users can upload data to Google Drive to complete training, eliminating hardware limitations.

Clinical Significance：The Brain Age Gap can serve as a biomarker for identifying high-risk groups for neurodegenerative diseases, evaluating disease progression and intervention effects, and studying the impact of lifestyle on brain health. Future Directions：Multi-modal fusion (structural MRI + functional MRI + DTI), training on large-scale datasets (e.g., UK Biobank), interpretability research (Grad-CAM visualization), and longitudinal prediction (estimation of individual brain aging rate).