| dc.description.abstract | With the aging population, Alzheimer’s disease has become the most common type of de
mentia, severely affecting the quality of life of patients. This study aims to explore a diagnostic
method that combines ventricular volume and cognitive assessment scales to achieve superior
performance in diagnosing Alzheimer’s disease. First, we preprocessed the data from 3D mag
netic resonance imaging (MRI) to calculate the ventricular area in each slice, and then summed
these areas through slice stacking to obtain the total ventricular volume. Next, we input the
ventricular volume and cognitive assessment scale data into a fully connected layer neural net
work in deep learning to distinguish between cognitively normal controls (CN), mild cognitive
impairment patients (MCI), and Alzheimer’s disease patients (AD).
The results showed that the accuracy of the ternary classification is 0.87, the accuracy of
distinguishing between cognitively normal and mild cognitive impairment is 0.95, the accu
racy of distinguishing between mild cognitive impairment and Alzheimer’s disease is 0.84, and
the accuracy of distinguishing between cognitively normal and Alzheimer’s disease is 0.99. The
results indicate that there is a correlation between increased ventricular volume and cognitive de
cline, providing new insights for the early diagnosis and progression monitoring of Alzheimer’s
disease.
However, there is still room for improvement in distinguishing between mild cognitive im
pairment and Alzheimer’s disease. Future work could focus on incorporating features highly
correlated with Alzheimer’s disease to improve the accuracy of the diagnostic method. In con
clusion, the comprehensive diagnostic method proposed in this study has potential in improving
the accuracy of Alzheimer’s disease diagnosis, providing an important reference for future re
search and clinical applications. | en_US |