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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A Deep Learning approach for Diagnosis of Mild Cognitive Impairment Based on MRI Images
Hamed Taheri Gorji1, Naima Kaabouch2
1Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58202-7165, USA.
Abstract:
Mild cognitive impairment (MCI) is an intermediary stage condition between healthy people and Alzheimer's disease (AD) patients and other dementias. AD is a progressive and irreversible neurodegenerative disorder, which is a significant threat to people, age 65 and older. Although MCI does not always lead to AD, an early diagnosis at the stage of MCI can be very helpful in identifying people who are at risk of AD. Moreover, the early diagnosis of MCI can lead to more effective treatment, or at least, significantly delay the disease's progress, and can lead to social and financial benefits. Magnetic resonance imaging (MRI), which has become a significant tool for the diagnosis of MCI and AD, can provide neuropsychological data for analyzing the variance in brain structure and function. MCI is divided into early and late MCI (EMCI and LMCI) and sadly, there is no clear differentiation between the brain structure of healthy people and MCI patients, especially in the EMCI stage. This paper aims to use a deep learning approach, which is one of the most powerful branches of machine learning, to discriminate between healthy people and the two types of MCI groups based on MRI results. The convolutional neural network (CNN) with an efficient architecture was used to extract high-quality features from MRIs to classify people into healthy, EMCI, or LMCI groups. The MRIs of 600 individuals used in this study included 200 control normal (CN) people, 200 EMCI patients, and 200 LMCI patients. This study randomly selected 70 percent of the data to train our model and 30 percent for the test set. The results showed the best overall classification between CN and LMCI groups in the sagittal view with an accuracy of 94.54 percent. In addition, 93.96 percent and 93.00 percent accuracy were reached for the pairs of EMCI/LMCI and CN/EMCI, respectively.
Insights
A deep learning model accurately distinguished between healthy individuals and those with early or late mild cognitive impairment (MCI) using MRI scans. This AI approach shows promise for early detection of cognitive decline and potential Alzheimer's disease risk.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Neurology
Background:
- Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease (AD), necessitating early detection for effective management.
- Distinguishing between healthy controls (CN), early MCI (EMCI), and late MCI (LMCI) using brain structure alone is challenging.
- Magnetic resonance imaging (MRI) provides valuable data for analyzing brain structure and function related to cognitive decline.
Purpose of the Study:
- To develop and evaluate a deep learning model for classifying individuals into CN, EMCI, or LMCI groups based on MRI data.
- To assess the efficacy of a convolutional neural network (CNN) in extracting discriminative features from brain MRIs.
- To investigate the potential of AI in improving the accuracy of MCI diagnosis.
Main Methods:
- A convolutional neural network (CNN) architecture was employed to analyze MRI scans from 600 individuals (200 CN, 200 EMCI, 200 LMCI).
- The dataset was randomly split into 70% for training and 30% for testing the classification model.
- The model was trained to classify subjects into healthy, EMCI, or LMCI categories using MRI features.
Main Results:
- The CNN achieved high classification accuracy, with the best performance in distinguishing between CN and LMCI groups (94.54%) in the sagittal view.
- Accurate classification was also demonstrated between EMCI and LMCI groups (93.96%) and between CN and EMCI groups (93.00%).
- The deep learning approach effectively identified differences in brain structure indicative of different cognitive states.
Conclusions:
- Deep learning, specifically CNNs, can effectively differentiate between healthy individuals and those with early or late mild cognitive impairment using MRI.
- This AI-driven approach offers a promising tool for the early and accurate diagnosis of MCI, potentially aiding in the prediction of Alzheimer's disease risk.
- High classification accuracies suggest that MRI combined with deep learning can provide valuable insights into subtle neuroanatomical changes associated with cognitive impairment.
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