Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease.

Qun Shang,Wei Liu,Xiaowang Bi,Enas Abdulhay,Huaiqin Liu

doi:10.1155/2021/8198552

Abstract

The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Network) and DML algorithms. Then, the system was used to classify AD, HC, and MCI, and the two algorithms were compared for the accuracy and stability of in classification of MRI images. It was found that in the classification of AD and HC, the classification accuracy and sensitivity of the deep measurement learning model are both 0.83, superior to the CNN model; in terms of specificity, the classification specificity of the DML model was 0.82, slightly lower than that of the CNN model; and that in the classification of MCI and HC, the classification accuracy and sensitivity of the DML model was 0.65, superior to the CNN model; and in terms of specificity, the classification specificity of the DML model was 0.66, slightly lower than that of the CNN model. It suggested that the DML model demonstrated better classification effects on early AD patients. The loss curve analysis results showed that, for classification of AD and HC or MCI and HC, the DML algorithm can improve the convergence speed of the AD early prediction model. Therefore, the DML algorithm can significantly improve the clarity and quality of MRI images, elevate the classification accuracy and stability of early AD patients, and accelerate the convergence of the model, providing a new way for early prediction of AD.

Highlights

Alzheimer’s Disease (AD) is a chronic neurodegenerative cognitive disease
Experimental data. e experimental data of this study were from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database [12]. e Magnetic Resonance Imaging (MRI) image data used in this article were divided into 3 categories, namely: 180 cases of AD, including 88 cases of females, and 92 cases of males; 188 samples of Healthy Controls (HC), including 90 cases of females and 98 cases of males; and 210 samples of Mild Cognitive Impairment (MCI), including 104 females and 106 males. ere was no significant difference in age and gender of the three types of subjects, and the average age was approximately 74 years old
MRI images of AD patients based on Convolutional Neural Networks (CNN) and Deep metric learning (DML) algorithms

Summary

Introduction

Alzheimer’s Disease (AD) is a chronic neurodegenerative cognitive disease. At the onset of the disease, patients will suffer from cognitive dysfunction such as memory loss and language function loss [1]. The cause of Alzheimer’s disease is still unclear, and the condition is irreversible, and a thorough treatment of the disease has not yet been developed [3]. When AD is diagnosed in the early stage can it be possible to slow down or inhibit the progression. Us, early prediction and diagnosis of AD is very important and meaningful in clinical treatment. Mild Cognitive Impairment (MCI) is a cognitive dysfunction between Alzheimer’s disease (AD) and Healthy Controls (HC) [4]. Most patients with MCI have developed AD [5]

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Discussion

Conclusion