The Cholinergic Pathways Hyperintensities Scale (CHIPS) is a visual rating
scale used to assess the extent of cholinergic white matter hyperintensities in
T2-FLAIR images, serving as an indicator of dementia severity. However, the
manual selection of four specific slices for rating throughout the entire brain
is a time-consuming process. Our goal was to develop a deep learning-based
model capable of automatically identifying the four slices relevant to CHIPS.
To achieve this, we trained a 4-class slice classification model (BSCA) using
the ADNI T2-FLAIR dataset (N=150) with the assistance of ResNet. Subsequently,
we tested the model's performance on a local dataset (N=30). The results
demonstrated the efficacy of our model, with an accuracy of 99.82% and an
F1-score of 99.83%. This achievement highlights the potential impact of BSCA as
an automatic screening tool, streamlining the selection of four specific
T2-FLAIR slices that encompass white matter landmarks along the cholinergic
pathways. Clinicians can leverage this tool to assess the risk of clinical
dementia development efficiently.

通过训练基于深度学习的模型，我们成功地开发出一种能够自动识别与 CHIPS 相关的四个切片的方法，该方法能够在评估痴呆严重程度方面作为一个指标，并且具有 99.82% 的准确率和 99.83% 的 F1 分数。这一成就突显了 BSCA 作为一种自动筛查工具的潜在影响，它能够简化在沿着胆碱能通路中涵盖白质标志物的四个特定 T2-FLAIR 切片的选择过程，临床医生可以利用该工具高效地评估临床痴呆的风险。