Alzheimer’s Disease Prediction with Irregular MRI Sequences Based on Pyramid Squeeze Attention and Time-Sensitive Attention Mechanisms^†

Alzheimer’s disease (AD) often leads to cognitive impairments and behavioral issues, making early detection and treatment crucial for slowing the progression of the disease and improving quality of life. This type of dementia typically worsens gradually, so accurately predicting the course of the disease is critical for medical intervention. To tackle it, this paper develops a new type of time series Alzheimer’s disease prediction model (ChaoJiBang-Net). This model integrates pyramid squeeze attention mechanisms, time position encoding, and time-sensitive attention techniques. It is specifically designed for irregular sampling and variable-length sequences in MRI images. The model aims to predict specific time points for Alzheimer’s disease, which are arbitrarily chosen. Experimental results show that increasing the amount of temporal data (time steps) can significantly improve the model’s prediction performance, as reflected in the progressively higher AUC values. Furthermore, this model can perform accurate predictions at any number of time steps, reducing reliance on frequent patient follow-ups and enhancing its clinical applicability. Currently, the predictive accuracy of this model, using MRI single modality data, surpasses the highest standards of existing state-of-the-art (SOTA) technologies.