De Silva, Kevin; Kunz, Holger; (2023) Prediction of Alzheimer's Disease from Magnetic Resonance Imaging using a Convolutional Neural Network. Intelligence-Based Medicine , 7 , Article 100091. 10.1016/j.ibmed.2023.100091.

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Abstract

OBJECTIVES: The primary goal of this study is to examine if a convolutional neural network (CNN) can be applied as a diagnostic tool for predicting Alzheimer's Disease (AD) from magnetic resonance imaging (MRI) using the MIRIAD-dataset (Minimal Interval Resonance Imaging in Alzheimer's Disease) from one single central slice of the brain. METHODS: The MIRIAD dataset contains patients' health records represented by a set of MRI scans of the brain and further diagnostic data. Hyperparameters and configurations of CNNs were optimized to determine the best-performing model. The CNN was implemented in Python with the deep learning library ‘Keras’ using Linux/Ubuntu as the operating system. RESULTS: This study obtained the following best performance metrics for predicting Alzheimer's Disease from MRI with Matthew's Correlation Coefficient (MCC) of 0.77; accuracy of 0.89; F1-score of 0.89; AUC of 0.92. The computational time for the training of a CNN takes less than 30 sec. s with a GPU (graphics processing unit). The prediction takes less than 1 sec. on a standard PC. CONCLUSIONS: The study suggests that an axial MRI scan can be used to diagnose if a patient has Alzheimer's Disease with an AUC score of 0.92.

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