Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for
1From the Department of Radiology and Research Institute of Radiology (C.H.S., W.H.S., S.J.K.).
AJNR. American Journal of Neuroradiology
|November 6, 2020
Summary
A new deep learning algorithm accurately diagnoses Alzheimer disease using brain MRI scans. This method shows promise for early and widespread detection of Alzheimer disease.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Medical Diagnostics
Background:
- Deep learning shows potential for predicting Alzheimer disease from T1-weighted brain MR images.
- Existing methods for Alzheimer disease diagnosis can be limited.
Purpose of the Study:
- To develop and validate a deep learning algorithm for Alzheimer disease diagnosis.
- Utilize 3D T1-weighted brain MR images for automated segmentation and classification.
Main Methods:
- Developed a 2-step deep learning algorithm using convolutional neural networks for brain parcellation.
- Employed XGBoost classifier, compared with logistic regression and Support Vector Machine, for disease prediction.
- Utilized 5-fold cross-validation across four datasets totaling 2637 patients.
Main Results:
- XGBoost significantly improved Alzheimer disease prediction compared to other methods (P < .001).
- For differentiating Alzheimer disease from mild cognitive impairment, XGBoost achieved an AUC of 0.825 with 68% sensitivity and 70% specificity.
- For differentiating mild cognitive impairment from healthy controls, XGBoost achieved an AUC of 0.870 with 79% sensitivity and 80% specificity.
Conclusions:
- The deep learning algorithm enables accurate Alzheimer disease diagnosis from T1-weighted brain MR images.
- The algorithm's reliance on widely available T1-weighted MRI makes it a promising tool for widespread Alzheimer disease prediction.
