Classifying Dementia Severity Using MRI Radiomics Analysis of the Hippocampus and Machine Learning
Dementia is a leading cause of global mortality, with limited treatment options available. Patients progress through three stages: cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Accurate prediction of these stages can significantly slow deterioration...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10723313/ |
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| Summary: | Dementia is a leading cause of global mortality, with limited treatment options available. Patients progress through three stages: cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Accurate prediction of these stages can significantly slow deterioration. Recent research suggests analyzing MRI images, focusing on markers like whole brain, hippocampus, and entorhinal cortex atrophy, is diagnostically valuable. However, most methods analyze 2D MRI images, losing anatomical context. Our study processes 3D MRI data with AC-PC alignment correction and extracts radiomic features from the segmented hippocampus, followed by discretization and principal component analysis for optimization. Machine learning and deep learning methods are then used for classification with cross-validation, achieving the highest accuracy with support vector machine (SVM). Compared with other studies, our results show the highest accuracy among the three dementia severity classifications. Combining the radiomic features from segmented hippocampus in MRI with machine learning promises unprecedented accuracy in predicting dementia severity, potentially slowing progression and enhancing patients’ quality of life. |
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| ISSN: | 2169-3536 |