Aβ۴۲/۴۰ ratio prediction using MRI images features for Alzheimer’s Early Detection

Abstract— Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and the accumulation of amyloid-beta plaques. Early detection is crucial for timely intervention, and the Aβ۴۲/Aβ۴۰ ratio is a key biomarker for identifying amyloid deposition. In this study, we propose a method to predict the Aβ۴۲/Aβ۴۰ ratio using the extracted features from MRI images using ۳D Convolutional Neural Network (۳D CNN). Moreover, Random Forest Regression is employed to obtain the relationship between MRI features and the Aβ۴۲/Aβ۴۰ ratio. Our results demonstrate a strong correlation (r = ۰.۷۲) between the predicted and actual Aβ۴۲/Aβ۴۰ ratios, effectively predicting amyloid accumulation. This result also makes the proposed feature extraction model more reliable. By leveraging MRI and molecular biomarkers such as the Aβ۴۲/Aβ۴۰ ratio, the proposed method provides valuable insights into disease progression and early diagnosis. By leveraging MRI and molecular biomarkers such as the Aβ۴۲/Aβ۴۰ ratio, the proposed method provides valuable insights into disease progression and early diagnosis.

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Keywords—۳DCNN ، Alzheimer’s Disease ، Aβ۴۲/Aβ۴۰ ratio ، MRI ، Random Forest Regression