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Updated: Jun 26, 2025
Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
Published on: February 19, 2021
Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis
Tareef S Daqqaq1, Ayman S Alhasan1, Hadeel A Ghunaim1
1From the Department of Internal Medicine (Daqqaq, Alhasan, Ghunaim),College of Medicine, Taibah University, Madinah, and from Department of Radiology (Daqqaq), Prince Mohammed Bin Abdulaziz Hospital, Ministry of National Guard Health Affairs, and from the Department of Radiology (Alhasan), King Faisal Specialist Hospital and Research Center, Madinah, Kingdom of Saudi Arabia.
Objectives:
The brain and spinal cord, constituting the central nervous system (CNS), could be impacted by an inflammatory disease known as multiple sclerosis (MS). The convolutional neural networks (CNN), a machine learning method, can detect lesions early by learning patterns on brain magnetic resonance image (MRI). We performed this study to investigate the diagnostic performance of CNN based MRI in the identification, classification, and segmentation of MS lesions.
Methods:
PubMed, Web of Science, Embase, the Cochrane Library, CINAHL, and Google Scholar were used to retrieve papers reporting the use of CNN based MRI in MS diagnosis. The accuracy, the specificity, the sensitivity, and the Dice Similarity Coefficient (DSC) were evaluated in this study.
Results:
In total, 2174 studies were identified and only 15 articles met the inclusion criteria. The 2D-3D CNN presented a high accuracy (98.81, 95% CI: 98.50-99.13), sensitivity (98.76, 95% CI: 98.42-99.10), and specificity (98.67, 95% CI: 98.22-99.12) in the identification of MS lesions. Regarding classification, the overall accuracy rate was significantly high (91.38, 95% CI: 83.23-99.54). A DSC rate of 63.78 (95% CI: 58.29-69.27) showed that 2D-3D CNN-based MRI performed highly in the segmentation of MS lesions. Sensitivity analysis showed that the results are consistent, indicating that this study is robust.
Conclusion:
This metanalysis revealed that 2D-3D CNN based MRI is an automated system that has high diagnostic performance and can promptly and effectively predict the disease.
