Using ResNet to utilize 4-class T2-FLAIR slice classification based on the cholinergic pathways hyperintensities scale for pathological aging

Motivation: Cholinergic Pathways Hyperintensities Scale (CHIPS) is a visual rating scale to evaluate the burden of cholinergic white matter hyperintensities in T2-FLAIR image, indicating the severity of dementia. However, it is still time-consuming to screen slices throughout the whole brain to choose 4 specific slices for rating. Goal(s): To develop a deep-learning-based model to automatically select 4 slices specific to CHIPS. Approach: We used ADNI T2-FLAIR dataset (N=150) to train a 4-class slice classification model (BSCA) utilized by ResNet, and a local dataset (N=30) to test its performance. Results: Our model achieved the accuracy of 99.82% and F1-score of 99.83%. Impact: BSCA can be an automatic screening tool to efficiently provide 4 specific T2-FLAIR slices covering the white matter landmarks along the cholinergic pathways for clinicians to help evaluate whether patients have the high risk to develop clinical dementia.